Model Qualification Research Articles

Qualification of MELCOR and RELAP5 models for EU HCPB TBS accident analyses

The F4E, Amec Foster Wheeler and INL comprehensive methodology for fusion breeding blanket accident analysis, published last year, consists of several phases. Following the selection of the reference accident scenarios, development of detailed accident analysis specifications and assessment of the analysis codes, the Test Blanket Systems (TBS) models are constructed using the selected codes and modelling approaches. The models are then qualified according to a test matrix by comparison with Test Blanket Module (TBM) finite element design analyses, code-to-code comparisons of TBS normal operation and transient cases and sensitivity studies of accident scenarios. The test cases executed gradually move from qualification of separate systems models to complete TBS models and from the simulation of steady state and normal plasma pulses operation to consideration of power excursion, operational transients and accident events. Finally, both qualified models are used to analyse a selected accident scenario of 32h loss-of-offsite power with sensitivity studies dedicated to the uncertainty evaluation. This step completes the qualification process. The qualification results obtained for the EU Helium-Cooled Pebble-Bed (HCPB) TBS models are reported in the paper.

The Predictive Power of Ground‐Motion Prediction Equations

Although model calibration should be always performed alongside model validation, this is rarely the case when deriving new ground‐motion prediction equations (GMPEs). Explanatory modeling (Shmueli, 2010) is often preferred to data‐driven predictive approaches, and the analysis of the residual distribution is generally used to support the model qualification. Following previous studies (Kuehn et al. , 2009; Scherbaum et al. , 2009), this work aims to again stress the importance of validation for assessing the predictive power of GMPEs and for avoiding, or at least limiting, data overfitting. Considering the strong‐motion data and models recently analyzed by Roselli et al. (2016), I exemplify the application of standard validation approaches based on predictive metrics (Akaike and Bayesian information criteria), resample techniques (cross validation and bootstrap), and validation against new data. The results confirm that GMPEs overfitting the calibration data have a limited predictive power, whereas, regarding validation against new data, the selection of the validation data set should take into account possible regional effects in the ground motion.

(342) Oliceridine (TRV130), a novel μ receptor G protein pathway selective modulator (μ-GPS), demonstrates a predictable relationship between plasma concentrations and pain relief. I: development of a pharmacokinetic/pharmacodynamic (PK/PD) model

Conventional opioids bind to μ-opioid receptors and activate two downstream signaling pathways: G protein coupling, linked to analgesia, and ß-arrestin recruitment, linked to ORAEs and limiting efficacy. Oliceridine (TRV130) is a novel μ receptor G protein Pathway Selective modulator (μ-GPS) that differentially activates G protein coupling while mitigating ß-arrestin recruitment. Unlike morphine, oliceridine has no known active metabolites; therefore, analgesic efficacy is predictably linked to its concentration in the plasma. Using data derived from both Phase 1 studies in healthy volunteers as well as data from a Phase 2 study examining the efficacy of oliceridine for the treatment of post-bunionectomy pain, we have developed a mathematical model linking the PK of oliceridine to its effect on the numeric pain rating scale score (NPRS). Phase 1 data consisted of 145 subjects (88% male, 12% female), who received single doses of oliceridine ranging between 0.15-7mg, as well as multiple doses ranging from 0.4-4.5mg Q4-6 hours. Sixteen of these subjects were CYP2D6 poor metabolizers, who have lower oliceridine clearance than extensive metabolizers. Approximately 265 subjects (10% male, 90% female) came from the Phase 2 study, where they received active doses ranging from 0.5-4mg Q 3-4 hours. Placebo control as well as an active comparator (morphine), were also included in this study. The morphine group was not used in model development. The final PK model was a three-compartment model which included covariates of body weight, sex, and CYP2D6 status. The PD model was an indirect response model linked to plasma oliceridine concentrations, and included the placebo pain response over the 48-hour treatment period. The EC50 for oliceridine on pain relief was estimated as 10.1 ng/mL (95% CI 8.4-12.1ng/mL). Model qualification showed that the model robustly reproduced the original data. This study was supported by Trevena, Inc.

Nonlinear mixed effects modelling for the analysis of longitudinal body core temperature data in healthy volunteers

Many longitudinal studies have collected serial body core temperature (Tc) data to understand thermal work strain of workers under various environmental and operational heat stress environments. This provides the opportunity for the development of mathematical models to analyse and forecast temporal Tc changes across populations of subjects. Such models can reduce the need for invasive methods that continuously measure Tc. This current work sought to develop a nonlinear mixed effects modelling framework to delineate the dynamic changes of Tc and its association with a set of covariates of interest (e.g. heart rate, chest skin temperature), and the structure of the variability of Tc in various longitudinal studies. Data to train and evaluate the model were derived from two laboratory investigations involving male soldiers who participated in either a 12 (N = 18) or 15 km (N = 16) foot march with varied clothing, load and heat acclimatisation status. Model qualification was conducted using nonparametric bootstrap and cross validation procedures. For cross validation, the trajectory of a new subject’s Tc was simulated via Bayesian maximum a posteriori estimation when using only the baseline Tc or using the baseline Tc as well as measured Tc at the end of every work (march) phase. The final model described Tc versus time profiles using a parametric function with its main parameters modelled as a sigmoid hyperbolic function of the load and/or chest skin temperature. Overall, Tc predictions corresponded well with the measured data (root mean square deviation: 0.16 °C), and compared favourably with those provided by two recently published Kalman filter models.

A model qualification method for mechanistic physiological QSP models to support model-informed drug development.

Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision-making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug-specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.

Correlation of component human body model and charged device model qualification levels with electrical failures in electronics assembly

Electrostatic discharge sensitivity of integrated circuits is compared with electrical failure levels in electronics assembly. Electrical components with a low electrostatic discharge withstand voltage would be expected to have more electrical failures than more robust components. However, the analysis based on 47 products, 14 facilities, and 6 billion integrated circuits show no correlation between electrical failures and electrostatic discharge sensitivity of components. This was found when the withstand voltage of the components is equal or higher than 100 V human body model and 200 V charged device model.

Viral clearance for biopharmaceutical downstream processes

Viral clearance studies are mandated prior to entering clinical trials and for commercial launch of biopharmaceuticals. These studies are a key component of risk mitigation to reduce the potential for iatrogenic transmission of pathogenic viruses. This paper reviews regulatory guidance and practical strategies for designing viral clearance studies. Essential elements of a developmental phase-appropriate viral clearance package are detailed. These include scale-down model qualification, virus spike experiments and validation (clearance evaluation) of manufacturing process steps. Heuristics and learnings from available data are shared. Developments in this area including generic validation strategies, multiviral spiking strategies and use of newer model viruses for nonconventional substrates are also described. This review provides a framework for a comprehensive viral validation package for regulatory submissions.

Interval Methods for Model Qualification: Methodology and Advanced Application

An actual model in simulation (e.g. in chemistry) or control (e.g. in robotics) is often too complex to use, and sometimes impossible to obtain. To handle a system in practice, a simplification of the real model is often necessary. This simplification goes through some hypotheses made on the system or the modeling approach. These hypotheses are rarely verified whereas they could lead to an inadmissible model, over approximated for its use. In this paper, we propose a method that qualifies the simplification validity for all models that can be expressed by real-valued variables involved in closed-form relations and depending on parameters. We based our approach on a verification of a quality threshold on the hypothesis relevance. This method, based on interval analysis, checks the acceptance of the hypothesis in a full range of the whole model space, and gives bounds on the quality threshold and on the model parameters. Our approach is experimentally validated on a robotic application.

Population Pharmacokinetic Modeling of Veliparib (ABT-888) in Patients with Non-Hematologic Malignancies

Veliparib (ABT-888) is a potent oral inhibitor of Poly(ADP-ribose) polymerase enzyme that is currently in development for the treatment of non-hematologic and hematologic malignancies. This analysis characterizes the population pharmacokinetics of veliparib, including developing a structural pharmacokinetic model and testing patient demographics and covariates for potential influence on veliparib pharmacokinetics in patients with non-hematologic malignancies. The analysis dataset included 3,542 veliparib concentration values from 325 patients with non-hematologic malignancies enrolled in three phase I and one phase II studies. Population pharmacokinetic modeling was performed using NONMEM. The likelihood ratio test was used for comparison of nested models, and visual predictive check was employed for model qualification. Covariates tested included body size measures, creatinine clearance (CLCR), formulation, age, sex, race, liver function tests, and coadministration with temozolomide. A one-compartment model with first-order absorption and elimination adequately described veliparib pharmacokinetics. The final model included fixed effects for CLCR on veliparib oral clearance (CL/F) and lean body mass (LBM) on volume of distribution (V d/F). CL/F and V d/F were 20.9 L/h (for a CLCR of 100 mL/min) and 173 L (for an LBM of 56 kg), respectively. Only LBM and CLCR were found to be determinants of veliparib V d/F and CL/F, respectively. Dosage adjustments of veliparib on the basis of body size, age, sex, race, liver function, and temozolomide coadministration are not necessary in patients with non-hematologic malignancies. This is the first study to characterize the population pharmacokinetics of veliparib, and the developed model will be used to conduct simulations and evaluate veliparib exposure-response relationships.

Model validation based on in situ radionuclide migration tests in Boom Clay: status of a large-scale migration experiment, 24 years after injection

Abstract Demonstration of the long-term safety of a nuclear waste repository relies on earth science models, integrated in a performance assessment model chain. These models are subject to quality assurance procedures and principles of which model validation, qualification and verification are essential elements. However, in the context of performance assessment, model validation is often limited owing to extreme timescales and the use of natural barriers that can never be entirely characterized. Nevertheless, it is often possible to demonstrate that the models are valid or qualified to describe the processes at hand. In case of geological disposal in Boom Clay, the host formation is the dominant barrier for radionuclide migration and releases to the biosphere. Therefore, it has to be demonstrated that migration of solutes through Boom Clay at relevant scale is adequately understood. Large-scale and long-term in situ migration experiments, such as the CP1 experiment, form a cornerstone in this confidence-building process. In this experiment, accurate predictions of the tracer's breakthrough curves up to 3 m from the source have been obtained using the conventional advection–dispersion–reaction equation to describe solute transport and parameters obtained from (small-scale) migration experiments in the laboratory.

Building the Strategic National Stockpile Through the NIAID Radiation Nuclear Countermeasures Program

The possibility of a public health radiological or nuclear emergency in the United States remains a concern. Media attention focused on lost radioactive sources and international nuclear threats, as well as the potential for accidents in nuclear power facilities (e.g., Windscale, Three Mile Island, Chernobyl, and Fukushima) highlight the need to address this critical national security issue. To date, no drugs have been licensed to mitigate/treat the acute and long-term radiation injuries that would result in the event of large-scale, radiation, or nuclear public health emergency. However, recent evaluation of several candidate radiation medical countermeasures (MCMs) has provided initial proof-of-concept of efficacy. The goal of the Radiation Nuclear Countermeasures Program (RNCP) of the National Institute of Allergy and Infectious Diseases (National Institutes of Health) is to help ensure the government stockpiling of safe and efficacious MCMs to treat radiation injuries, including, but not limited to, hematopoietic, gastrointestinal, pulmonary, cutaneous, renal, cardiovascular, and central nervous systems. In addition to supporting research in these areas, the RNCP continues to fund research and development of decorporation agents targeting internal radionuclide contamination, and biodosimetry platforms (e.g., biomarkers and devices) to assess the levels of an individual's radiation exposure, capabilities that would be critical in a mass casualty scenario. New areas of research within the program include a focus on special populations, especially pediatric and geriatric civilians, as well as combination studies, in which drugs are tested within the context of expected medical care management (e.g., antibiotics and growth factors). Moving forward, challenges facing the RNCP, as well as the entire radiation research field, include further advancement and qualification of animal models, dose conversion from animal models to humans, biomarker identification, and formulation development. This paper provides a review of recent work and collaborations supported by the RNCP.

Development of a scale down cell culture model using multivariate analysis as a qualification tool

In characterizing a cell culture process to support regulatory activities such as process validation and Quality by Design, developing a representative scale down model for design space definition is of great importance. The manufacturing bioreactor should ideally reproduce bench scale performance with respect to all measurable parameters. However, due to intrinsic geometric differences between scales, process performance at manufacturing scale often varies from bench scale performance, typically exhibiting differences in parameters such as cell growth, protein productivity, and/or dissolved carbon dioxide concentration. Here, we describe a case study in which a bench scale cell culture process model is developed to mimic historical manufacturing scale performance for a late stage CHO-based monoclonal antibody program. Using multivariate analysis (MVA) as primary data analysis tool in addition to traditional univariate analysis techniques to identify gaps between scales, process adjustments were implemented at bench scale resulting in an improved scale down cell culture process model. Finally we propose an approach for small scale model qualification including three main aspects: MVA, comparison of key physiological rates, and comparison of product quality attributes.

The way to a design space for an animal cell culture process according to Quality by Design (QbD)

Background The strategy of implementation of the QbD (Quality by design) approach in upstream processing of therapeutic proteins consists of the identification of critical process parameters (CPPs) that have a statistically significant influence on the critical quality attributes (CQAs) of a specific process. By applying the acceptance criteria to the CQAs, proven acceptable ranges (PARs) for the CPPs can be deduced from experimental data. The multidimensional combination of these ranges form the design space and thus assures the quality of the product. The QbD approach according to the ICH guidelines Q8, Q9 and Q10 may be subdivided in the work packages scale down model qualification, risk analysis, process characterization and range studies. The foundation of the QbD approach is represented by the scale down model. Several different scale down criteria were applied and adapted until a satisfactory match of scale down to commercial scale data was achieved. The scale down model is then used to investigate cause effect relationships between process parameters and quality attributes of the production process. Since a standard cell culture process from thawing of the vial up to the final production fermenter can comprise up to 100 process parameters, a risk based approach is helpful to filter the most important ones. Those parameters are then experimentally investigated to verify their criticality for the quality attributes of the process. This approach relies on design of experiment (DoE) to reduce the number of required experiments to a manageable number while maintaining meaningful results. During the range studies, those critical parameters will be investigated with the help of a high resolution DoE matrix in order to be able to reveal possible interactions and higher order effects.

Application of quality by design principles to the development and technology transfer of a major process improvement for the manufacture of a recombinant protein

This study describes the application of quality by design (QbD) principles to the development and implementation of a major manufacturing process improvement for a commercially distributed therapeutic protein produced in Chinese hamster ovary cell culture. The intent of this article is to focus on QbD concepts, and provide guidance and understanding on how the various components combine together to deliver a robust process in keeping with the principles of QbD. A fed-batch production culture and a virus inactivation step are described as representative examples of upstream and downstream unit operations that were characterized. A systematic approach incorporating QbD principles was applied to both unit operations, involving risk assessment of potential process failure points, small-scale model qualification, design and execution of experiments, definition of operating parameter ranges and process validation acceptance criteria followed by manufacturing-scale implementation and process validation. Statistical experimental designs were applied to the execution of process characterization studies evaluating the impact of operating parameters on product quality attributes and process performance parameters. Data from process characterization experiments were used to define the proven acceptable range and classification of operating parameters for each unit operation. Analysis of variance and Monte Carlo simulation methods were used to assess the appropriateness of process design spaces. Successful implementation and validation of the process in the manufacturing facility and the subsequent manufacture of hundreds of batches of this therapeutic protein verifies the approaches taken as a suitable model for the development, scale-up and operation of any biopharmaceutical manufacturing process.

Visualization Concepts to Enhance Quantitative Decision Making in Drug Development

Communication of analysis and simulation results via graphical presentation is a cornerstone of pharmacometric analysis. A modeling and simulation (M&S) project typically has 3 distinct phases: (1) exploratory data analysis, (2) model building, and (3) simulation. Each phase has unique requirements for presentation of graphics. In exploratory data analysis, initial considerations include examination of data quality and discovery of key relationships in the data leading to identification of a modeling approach. For model building, assessment of model fit and model qualification are the objectives of graphical analysis. In the simulation phase, graphical displays expose relevant implications of the relationships described by the models in order to guide decision making. This article describes general considerations to aid in communication via graphical displays, accompanied by example graphs that illustrate the principles for each phase of an M&S project.

Formalization and qualification of models adapted to preliminary design

We propose a procedure for the formalization and qualification of models adapted specifically to the requirements of preliminary design. Using this methodology the current lack of suitable decision support tools for design can be overcome. Following a needs assessment for modeling during this design phase, the approach that we develop here is based on functional decomposition to structure the system to be designed. By applying the formalization procedure defined here, the system can be analyzed using tools that are frequently used to good effect by engineers and the problem of preliminary design is structured to produce a behavior model adapted to making design choices and suitable for use with decision support tools.

Development and Qualification of a Pharmacodynamic Model for the Pronounced Inoculum Effect of Ceftazidime against Pseudomonas aeruginosa

Evidence is mounting in support of the inoculum effect (i.e., slow killing at large initial inocula [CFUo]) for numerous antimicrobials against a variety of pathogens. Our objectives were to (i) determine the impact of the CFUo of Pseudomonas aeruginosa on ceftazidime activity and (ii) to develop and validate a pharmacokinetic/pharmacodynamic (PKPD) mathematical model accommodating a range of CFUo. Time-kill experiments using ceftazidime at seven concentrations up to 128 mg/liter (MIC, 2 mg/liter) were performed in duplicate against P. aeruginosa PAO1 at five CFUo from 10(5) to 10(9) CFU/ml. Samples were collected over 24 h and fit by candidate models in NONMEM VI and S-ADAPT 1.55 (all data were comodeled). External model qualification integrated data from eight previously published studies. Ceftazidime displayed approximately 3 to 4 log(10) CFU/ml net killing at 10(6.2) CFUo and concentrations of 4 mg/liter (or higher), less than 1.6 log(10) CFU/ml killing at 10(7.3) CFUo, and no killing at 10(8.0) CFUo for concentrations up to 128 mg/liter. The proposed mechanism-based model successfully described the inoculum effect and the concentration-independent lag time of killing. The mean generation time was 28.3 min. The effect of an autolysin was assumed to inhibit successful replication. Ceftazidime concentrations of 0.294 mg/liter stimulated the autolysin effect by 50%. The model was predictive in the internal cross-validation and had excellent in silico predictive performance for published studies of P. aeruginosa ATCC 27853 for various CFUo. The proposed PKPD model successfully described and predicted the pronounced inoculum effect of ceftazidime in vitro and integrated data from eight literature studies to support translation from time-kill experiments to in vitro infection models.

Development and testing of analytical models for the pebble bed type HTRs

The pebble bed type gas cooled high temperature reactor (HTR) appears to be a good candidate for the next generation nuclear reactor technology. These reactors have unique characteristics in terms of the randomness in geometry, and require special techniques to analyze their systems. This study includes activities concerning the testing of computational tools and the qualification of models. Indeed, it is essential that the validated analytical tools be available to the research community. From this viewpoint codes like MCNP, ORIGEN and RELAP5, which have been used in nuclear industry for many years, are selected to identify and develop new capabilities needed to support HTR analysis. The geometrical model of the full reactor is obtained by using lattice and universe facilities provided by MCNP. The coupled MCNP-ORIGEN code is used to estimate the burnup and the refuelling scheme. Results obtained from Monte Carlo analysis are interfaced with RELAP5 to analyze the thermal hydraulics and safety characteristics of the reactor. New models and methodologies are developed for several past and present experimental and prototypical facilities that were based on HTR pebble bed concepts. The calculated results are compared with available experimental data and theoretical evaluations showing very good agreement. The ultimate goal of the validation of the computer codes for pebble bed HTR applications is to acquire and reinforce the capability of these general purpose computer codes for performing HTR core design and optimization studies.

Verification, validation, qualification and certification of enterprise models: Statements and opportunities

The first aim of this article is to demonstrate the high level of dependency between modeling activities and verification, validation, qualification and certification (VVQC) activities during the entire course of a project in an enterprise. It therefore aims to show the interest and relevance of VVQC to the enterprise-modeling domain, before highlighting the objectives to be achieved and requirements to be fulfilled by future work on VVQC. The first part introduces the concepts and definitions required. The second part describes the state of the art concerning the uses, best practices and main current research related to VVQC in the enterprise field. The third part proposes some orientations for future research to be prioritized in order to deal with crucial challenges in the enterprise. It appears that these research orientations have been applied for some years by both academic- and industry-based researchers.

Commentary on Dartois et al. – model building in population PK–PD analyses. A 2002–2004 literature survey

Commentary on Dartois <i>et al.</i> – model building in population PK–PD analyses. A 2002–2004 literature survey