Extrapolation Approach Research Articles

Accurately measuring slowly propagating flame speeds: Application to ammonia/air flames

Environmental concerns have driven the development of alternative fuels and refrigerant working fluids with low global warming potential. Ammonia (NH3) is a potential zero-carbon fuel, while hydrofluorocarbons (HFCs) like R-32 and R-1234yf are being adopted as refrigerants. When mixed with air, these compounds can sustain slowly propagating flames with laminar flame speeds less than 10 cm/s. Unlike typical hydrocarbon-fueled flames, these slow flames are influenced by buoyancy-induced flow and radiation heat loss. In this study, we experimentally investigate the flame speeds of NH3/air mixtures using the constant-pressure spherically expanding flame method, while circumventing gravity-induced natural convection, and account for radiation-induced inward flow. To mitigate buoyant convection, a low-cost drop tower was built and used to study slow spherically expanding flames in free fall. A computational model (SRADIF) is utilized that combines thermodynamic equilibrium and finite rate optically thin limit radiation heat loss calculations to estimate the inward flow. The developed methodology is utilized to investigate slowly propagating NH3/air flames over a range of equivalence ratios. A systematic approach was undertaken to understand and quantify the errors that could arise when deriving the laminar flame speed. It was found that attempting to study slowly propagating flames in a static configuration, as opposed to in free fall, results in large differences in flame dynamics and subsequently all derived quantities. It is necessary to study slowly propagating flames in free-fall. Additionally, using experimental data that has not been corrected for radiation-induced flow leads to large errors in all derived quantities. Furthermore, direct comparisons of experimental measurements and detailed flame simulations are found to be necessary to determine if existing extrapolation approaches are applicable to these slowly propagating flames, which are challenging to study.

Accurate and Fast Estimation of the Continuum Limit in Path Integral Simulations of Quantum Oscillators and Crystals.

Convergence of imaginary-time path integral results requires a substantial number of beads (n) when quantum effects are significant. Traditional Trotter scaling approaches estimate the continuum limit (n → ∞) through extrapolation; however, their validity is restricted to the asymptotic domain of large n. We introduce an efficient extrapolation approach for quantum oscillators and crystals with harmonic character. The fitting function is inspired by the analytic solution of the harmonic oscillator (HO), or the Einstein crystal for solids. While the formulation is designed for first derivative properties (energy and pressure), its extension to second derivative properties (e.g., heat capacity) is straightforward. We apply the method to a one-dimensional HO and anharmonic oscillator (AO), as well as a three-dimensional Lennard-Jones crystal. Configurations are sampled using path integral molecular dynamics simulations in the canonical ensemble, at a low temperature of T = 0.1 (simulation units). Compared to Trotter extrapolation approaches, the new method demonstrates a substantial accuracy in estimating the continuum limit, using only a few simulations of relatively small system sizes. This efficiency significantly reduces computational cost, providing a powerful tool to facilitate computations for more complex and challenging systems, such as molecules and crystals modeled using ab initio methods.

A joint time–space extrapolation approach within the Wiener path integral technique for efficient stochastic response determination of nonlinear systems

A joint time–space extrapolation approach within the Wiener path integral (WPI) technique is developed for determining, efficiently and accurately, the non-stationary stochastic response of diverse nonlinear dynamical systems. The approach can be construed as an extension of a recently developed space-domain extrapolation scheme to account also for the temporal dimension. Specifically, based on a variational principle, the WPI technique yields a boundary value problem (BVP) to be solved for determining a most probable path corresponding to specific final boundary conditions. Further, the most probable path is used for evaluating, approximately, a point of the system response joint probability density function (PDF) corresponding to a specific time instant. Remarkably, the BVP exhibits two unique features that are exploited in this paper for developing an efficient joint time–space extrapolation approach. First, the BVPs corresponding to two neighboring grid points in the spatial domain of the response PDF not only share the same equations, but also the boundary conditions differ only slightly. Second, information inherent in the time-history of an already determined most probable path can be used for evaluating points of the response PDF corresponding to arbitrary time instants, without the need for solving additional BVPs. In a nutshell, relying on the aforementioned unique and advantageous features of the WPI-based BVP, the complete non-stationary response joint PDF is determined, first, by calculating numerically a relatively small number of PDF points, and second, by extrapolating in the joint time–space domain at practically zero additional computational cost. Compared to a standard brute-force implementation of the WPI technique, the developed extrapolation approach reduces the associated computational cost by several orders of magnitude. Two numerical examples relating to an oscillator with asymmetric nonlinearities and fractional derivative elements, and to a nonlinear structure under combined stochastic and deterministic periodic loading are considered for demonstrating the reliability of the extrapolation approach. Juxtapositions with pertinent Monte Carlo simulation data are included as well.

Thermodynamic assessment of the Co-Cr-Fe-Ni quaternary system and diffusion study of its fcc phase

As a core of the high entropy alloys, the Co-Cr-Fe-Ni system has been widely investigated. In the present work, the thermodynamics of the Co-Cr-Fe-Ni system and the atomic mobilities of its fcc phase have been evaluated by means of the CALPHAD approach. First-principles calculations were performed to obtain the total energies for the end-member compounds of the σ phase in the Co-Cr-Fe-Ni system. Combining with the experimental data and thermodynamic modeling of the sub-systems from the literature, a set of self-consistent thermodynamic parameters were derived and extrapolated to obtain a thermodynamic description of the Co-Cr-Fe-Ni quaternary system. In order to verify the accuracy of the model parameters, the phase equilibria of a series of the CoCrxFeNi alloys with different Cr contents were determined using DSC, BSE and XRD analysis. Subsequently, based on the diffusion experimental data, the atomic mobilities of the fcc Cr-Fe-Ni alloys were reassessed using the DICTRA software. A mobility database for the fcc Co-Cr-Fe-Ni quaternary system was constructed by directly extrapolating the atomic mobilities of all sub-systems, and comprehensive comparisons prove the consistency between the present assessments and the experiments. In addition to the direct extrapolation approach, extra four-body interaction parameters concerning all four components were added and assessed. The results demonstrate that the extra interaction contributions are ignorable, so that the direct extrapolation from the sub-systems to the quaternary system is feasible in the fcc Co-Cr-Fe-Ni quaternary system.

Landscape connectivity for African elephants in the world's largest transfrontier conservation area: A collaborative, multi‐scalar assessment

Abstract Landscape connectivity operates at a variety of scales, depending on the geography of the area in question and the focal species or ecological process under consideration. Most connectivity studies, however, are typically focused on a single scale, which in the case of resistance‐based connectivity modelling, is often the entire landscape or protected area (PA) network. This large, single‐scale focus may miss areas that are important for connectivity at smaller scales and that can be documented via observed animal movements without resorting to landscape‐wide statistical modelling and extrapolation approaches. Here, we characterize landscape connectivity at three different scales (local/micro, inter‐PA, and landscape‐wide/macro), using observed animal movements rather than conventional resistance surface models, to produce a connectivity conservation blueprint for African elephants (Loxodonta africana) in the Kavango‐Zambezi transfrontier conservation area (KAZA) in southern Africa. This analysis is based on an extensive, high‐resolution GPS tracking database comprising approximately 4 million GPS locations from nearly 300 tagged elephants and their associated herds. Our results show that high‐fidelity elephant use of micro‐corridors is typically—though not exclusively—related to directed movements towards water, often amidst heavy anthropogenic presence. Movement pathways that connected KAZA's core protected areas were longer and variable, with some channelled into narrow areas of use and others more dispersed across larger sub‐landscapes. At the largest scale, a network analysis incorporating all used landscape grid cells revealed several clusters of large‐scale movement corridors that connected distant parts of KAZA. Synthesis and applications: Our three scales of analyses reveal disparate geographical priorities for connectivity conservation that collectively could help ensure the functional connectivity of KAZA for its largest inhabitants. Each scale will require its own set of inter‐related conservation interventions, while further research into areas with sparse data collection, and other species of conservation concern, could reveal additional connectivity priorities at each scale.

MPES-R: Multi-Parameter Evidence Synthesis in R for Survival Extrapolation-A Tutorial.

Survival extrapolation often plays an important role in health technology assessment (HTA), and there are a range of different approaches available. Approaches that can leverage external evidence (i.e. data or information collected outside the main data source of interest) may be helpful, given the extent of uncertainty often present when determining a suitable survival extrapolation. One of these methods is the multi-parameter evidence synthesis (MPES) approach, first proposed for use in HTA by Guyot et al., and more recently by Jackson. While MPES has potential benefits over conventional extrapolation approaches (such as simple or flexible parametric models), it is more computationally complex and requires use of specialist software. This tutorial presents an introduction to MPES for HTA, alongside a user-friendly, publicly available operationalisation of Guyot's original MPES that can be executed using the statistical software package R. Through two case studies, both Guyot's and Jackson's MPES approaches are explored, along with sensitivity analyses relevant to HTA. Finally, the discussion section of the tutorial details important considerations for analysts considering use of an MPES approach, along with potential further developments. MPES has not been used often in HTA, and so there are limited examples of how it has been used and perceived. However, this tutorial may aid future research efforts exploring the use of MPES further.

Ab initio study of the interaction between the charged system (FH+) and the He atom

We investigated the molecular structure and spectroscopic properties of the charged system (FH+) in interaction with a helium atom using an ab initio quantum chemistry approach in Jacobi coordinates. Our study focused on the ground state X2Π of (FH+) with various theoretical methods including UCCSD, UCCSD(T) and UCCSD(T)-F12, alongside basis sets like aug-cc-pVnZ (n = T, Q, 5, and 6) and cc-pVQZ-F12. We evaluated how these methods and basis sets affect the minimum energies We assessed the impact of the methods, basis sets, and extrapolation approaches on the minimum energies. Potential energy surfaces (PES) were generated using the correlated UCCSD(T)-F12 method with cc-pVQZ-F12 for hydrogen and fluorine, and cc-pVQZ-F12/optri for helium. These surfaces, considering spin-orbit coupling, are degenerate for linear geometries (θ=0° and θ=180°) of the (FH+)-He complex and correlate with the doubly degenerate X2Π state of (FH+). Our results reveal a strong anisotropic character at short and intermediate distances and a quasi-isotropic character upon dissociation into FH+ and He. We compared the spectroscopic parameters of the (FH+)-He complex with existing theoretical data, finding that the linear arrangement exhibits the highest stability, consistent with previous results for similar complexes. This conclusion is supported by the Milliken atomic charge distribution and a three-dimensional map of the Molecular Electrostatic Potential. Additionally, we used the LEVEL program, to calculate the bound rovibronic levels of the (FH+)-He complex for angular momentum values Ω=1/2 and Ω=3/2. Utilizing our ab initio results and a general interpolation approach based on the Reproducing Kernel Hilbert Space method, we generated contour maps illustrating the analytical potential for the (FH+)-He system. These findings will be employed to study the stabilities, geometries, energetics, and structures of the FH+ charged system within helium clusters.

Novel multivariate design concept for floating wind turbines by Gaidai multivariate reliability method and deconvolution scheme

Development of novel risk and reliability assessment methods is intended to support safer construction of offshore structures, subjected to environmental wave loads. Current study investigated 10-MW FWT (i.e., Floating Wind Turbine), operating under realistic environmental conditions. While increasing operating safety, enhanced risk and reliability assessment methods may eventually help reduce manufacturing and maintenance costs. Excessive structural dynamics being usually caused by environmental stressors, acting on structural system. Environmental loads resulting from ambient wind and wave motions are typical for offshore structures. Current work advocates a novel risk and reliability assessment methodology that allows for reliable forecasting of failure/damage risks, arising from excessive FWT structural dynamics. Recently developed Gaidai multivariate reliability methodology along with state-of-the-art deconvolution method had been employed. Unlike existing reliability approaches such as Weibull-type, GP (i.e., Generalized Pareto), POT (i.e., Peaks Over the Threshold), etc., the recommended methodology does not rely on any pre-assumed functional class, when extrapolating failure probability functional tail. Practical advantages of the suggested multivariate reliability methodology combined with deconvolution scheme over, that is, 4-parameter Weibull’s extrapolation method had been demonstrated. Suggested methodology makes effective use of even limited underlying datasets, enabling robust and accurate projections of multidimensional structural system failure/damage risks. Overall methodological performance suggests that numerically stable and accurate extreme dynamics forecasts for FWT structural bending moments might be obtained, utilizing suggested multivariate reliability methodology. Deconvolution extrapolation approach being more numerically stable than parametric extrapolation techniques, due to its non-parametric nature.

Current Challenges to Align Inflammatory Key Events in Animals and Lung Cell Models In Vitro.

With numerous novel and innovative in vitro models emerging every year to reduce or replace animal testing, there is an urgent need to align the design, harmonization, and validation of such systems using in vitro-in vivo extrapolation (IVIVE) approaches. In particular, in inhalation toxicology, there is a lack of predictive and prevalidated in vitro lung models that can be considered a valid alternative for animal testing. The predictive power of such models can be enhanced by applying the Adverse Outcome Pathways (AOP) framework, which casually links key events (KE) relevant to IVIVE. However, one of the difficulties identified is that the endpoint analysis and readouts of specific assays in in vitro and animal models for specific toxicants are currently not harmonized, making the alignment challenging. We summarize the current state of the art in endpoint analysis in the two systems, focusing on inflammatory-induced effects and providing guidance for future research directions to improve the alignment.

Evaluating the Application of Construction Projects Management Methodologies during Construction Projects Execution in Charities Organization

Over the past four decades, particularly in the Arabian Gulf region, there has been a significant increase of non-governmental charitable institutions leading to a wide spread of their humanitarian assistance, social services and diverse construction projects in impoverished and developing countries. Successful implementation of charitable construction projects requires proficient leadership, administrative accumen, and scientific expertise to achieve project objectives in terms of scope, time, cost and quality. This study delves into existing research on extrapolation, utilizating both quantitative and qualitative approaches using digital statistical analysis. By examining all the resolution points and axes, a set of essential criteria has been identified. The criteria can be considered as successful benchmarks for charitable organizations aiming to enhance the implementation of their construction projects.

Physiologically based Kinetic Modeling-Facilitated Quantitative In Vitro to In Vivo Extrapolation to Predict the Effects of Aloe-Emodin in Rats and Humans.

Aloe-emodin, a natural hydroxyanthraquinone, exerts both adverse and protective effects. This study aimed at investigating these potential effects of aloe-emodin in humans upon the use of food supplements and herbal medicines using a physiologically based kinetic (PBK) modeling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE) approach. For this, PBK models in rats and humans were established for aloe-emodin including its active metabolite rhein and used to convert in vitro data on hepatotoxicity, nephrotoxicity, reactive oxidative species (ROS) generation, and Nrf2 induction to corresponding in vivo dose-response curves, from which points of departure (PODs) were derived by BMD analysis. The derived PODs were subsequently compared to the estimated daily intakes (EDIs) resulting from the use of food supplements or herbal medicines. It is concluded that the dose levels of aloe-emodin from food supplements or herbal medicines are unlikely to induce toxicity, ROS generation, or Nrf2 activation in liver and kidney.

Exposure-Response Modeling in Adults and Adolescents With Schizophrenia to Support the Extrapolation of Brexpiprazole Efficacy to Adolescents.

In order to accelerate drug development and avoid unnecessary drug trials in vulnerable pediatric populations, the US Food and Drug Administration (FDA) released a general advice letter to sponsors permitting the effectiveness of atypical antipsychotics for the treatment of schizophrenia in adults to be extrapolated to adolescents. Extrapolation is based on the evidence-based assumptions that (1) disease characteristics and (2) response to therapy, are similar in adults and adolescents. Whereas the FDA validated the extrapolation approach using data from multiple drug development programs, aripiprazole data are the most relevant to confirm the validity of the extrapolation approach for brexpiprazole, since aripiprazole and brexpiprazole both modulate dopaminergic and serotonergic signaling in the brain. The aims of this analysis were (1) to quantitatively assess the aripiprazole exposure (average steady-state concentration)-response (Positive and Negative Syndrome Scale total score change from baseline) similarity between adults and adolescents with schizophrenia, (2) to extend the aripiprazole exposure-response modeling to brexpiprazole using adult data, and (3) to use the brexpiprazole model to predict schizophrenia symptom response in adolescents. Disease-drug-dropout models were developed using patient-level data from clinical studies of aripiprazole (1007 adults, 294 adolescents) and brexpiprazole (1235 adults) in schizophrenia. The aripiprazole model demonstrated similar exposure-response between adults and adolescents with schizophrenia, validating the extrapolation approach. Extrapolation of the brexpiprazole adult exposure-response model to adolescents predicted the efficacy of brexpiprazole in adolescents aged 13-17 years with schizophrenia.

A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks

Abstract. Accurate streamflow estimation is essential for effective water resource management and adapting to extreme events in the face of changing climate conditions. Hydrological models have been the conventional approach for streamflow interpolation and extrapolation in time and space for the past few decades. However, their large-scale applications have encountered challenges, including issues related to efficiency, complex parameterization, and constrained performance. Deep learning methods, such as long short-term memory (LSTM) networks, have emerged as a promising and efficient approach for large-scale streamflow estimation. In this study, we have conducted a series of experiments to identify optimal hybrid modeling schemes to consolidate physically based models with LSTM aimed at enhancing streamflow estimation in Denmark. The results show that the hybrid modeling schemes outperformed the Danish National Water Resources Model (DKM) in both gauged and ungauged basins. While the standalone LSTM rainfall–runoff model outperformed DKM in many basins, it faced challenges when predicting the streamflow in groundwater-dependent catchments. A serial hybrid modeling scheme (LSTM-q), which used DKM outputs and climate forcings as dynamic inputs for LSTM training, demonstrated higher performance. LSTM-q improved the mean Nash–Sutcliffe efficiency (NSE) by 0.22 in gauged basins and 0.12 in ungauged basins compared to DKM. Similar accuracy improvements were achieved with alternative hybrid schemes, i.e., by predicting the residuals between DKM-simulated streamflow and observations using LSTM. Moreover, the developed hybrid models enhanced the accuracy of extreme events, which encourages the integration of hybrid models within an operational forecasting framework. This study highlights the advantages of synergizing existing physically based hydrological models (PBMs) with LSTM models, and the proposed hybrid schemes hold the potential to achieve high-quality large-scale streamflow estimations.

Efficient Guided Wave Modelling for Tomographic Corrosion Mapping via One-Way Wavefield Extrapolation

Mapping corrosion depths along pipeline sections using guided-wave-based tomographic methods is a challenging task. Accurate defect sizing depends heavily on the precision of the forward model in guided wave tomography. This model is fitted to measured data using inversion techniques. This study evaluates the effectiveness of a recursive extrapolation scheme for tomography applications and full waveform inversion. It employs a table-driven approach, with precomputed extrapolation operators stored across a spectrum of wavenumbers. This enables fast modelling for extensive pipe sections, approaching the speed of ray tracing while accurately handling complex velocity models within the full frequency band. This ensures an accurate representation of diffraction phenomena. The study examines the assumptions underlying the extrapolation approach, namely, the negligible reflection and conversion of modes at defects. In our tomography approach, we intend to use multiple wave modes—A0, S0, and SH1—and helical paths. The acoustic extrapolation method is validated through numerical studies for different wave modes, solving the 3D elastodynamic wave equation. Comparison with an experimentally measured single-mode wavefield from an aluminium plate with an artificial defect reveals good agreement.

Physiologically based kinetic (PBK) modeling of propiconazole using a machine learning-enhanced read-across approach for interspecies extrapolation

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering − an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

Exploring the impact of solvent additives on dielectric properties in polymer photovoltaics: A deconvolution and in-depth study of electrical modulus, complex conductivity, and impedance responses

Impedance and dielectric spectroscopy is a well-known technique for characterizing interfaces, especially donor/receiver interfaces in bulk heterojunction (BHJ) solar cells. This technique was employed to investigate the interface of poly (4,8-bis-alkyloxybenzo(1,2-b:4,5-b0) dithio-phene-2,6-diyl-alt- (alkyl thieno (3,4-b) thiophene-2-carboxylate)-2,6-diyl) (PBDTTT-C): PC70BM in bulk heterojunction with DIO as additive. For this reason, a simulation was carried out to generate the complex impedance, modulus, and complex conductivity data based on the electrical parameters extracted from the equivalent circuit. Therefore, a new approach based on the coupling of complex functions such as impedance, modulus, and conductivity has been proposed. In addition, an extrapolation and deconvolution approaches are used to highlight the relaxation processes, the BHJ layer process, the two electrical contacts of the interfaces between PEDOT: PSS/PBDTTT-C: PC70BM and PBDTTT-C: PC70BM/Ca process, and the electrode polarization process. In the next step, the remove the low-frequency process enhances the visibility of other processes, particularly observable in the evolution of the electrical modulus and the conduction parameters associated with each process. In the complex functions, there was a good correlation between all parameters derived from both processes. The characteristics of the conduction processes, such as conductivity values at low and high frequencies and ionic strength were then extracted. All these parameters were evaluated and discussed as a function of the thickness of the active layer. Finally, we demonstrate that the electrical modulus and complex conductivity can be perfect tools to characterize the donor/acceptor interfaces.

Expert's experience-informed hierarchical kriging method for aerodynamic data modeling

Data-driven models, such as kriging, have gained popularity in aerospace engineering due to their capability of predicting multidimensional, nonlinear aerodynamic characteristics of an aircraft. However, they are still suffering from the problem associated with poor extrapolation capability and physical interpretability, which in turn has great impacts on aerodynamic performance and flight safety. To address this problem, this article proposes to incorporate an empirical aerodynamic model obtained from expert's understanding of aerodynamics in the fitting process of a kriging model. First, empirical aerodynamic models based on expert's understanding and experience are derived. Second, the regression term of a kriging model is replaced by the expert's experience-informed model so that the global trend can be consistent with physical laws and provides extra knowledge in the subregion(s) without training data, especially for the extrapolation region(s). Finally, the expert's experience-informed hierarchical kriging (EEI-HK) model is built in a sequential way. The proposed method is validated with analytical test examples and demonstrated by aerodynamic data modeling of an AGARD-B missile and an FDL-5A hypersonic flight vehicle. Results show that, compared with ordinary and universal kriging models, the proposed EEI-HK model can dramatically improve the prediction accuracy in the extrapolation domain and slightly enhance the interpolation accuracy, with assistance from physical information provided by an empirical model. In consequence, it can be a promising approach for aerodynamic data extrapolation and saving the cost of establishing an aerodynamic database.

Evolving Evidence-Based Value Assessment of One-Time Therapies: Tisagenlecleucel as a Case Study.

Economic evaluation of one-time therapies during reimbursement decision-making is challenging due to uncertain long-term outcomes. The availability of 5-year outcome data from the ELIANA trial and real-world evidence of tisagenlecleucel, the first chimeric antigen receptor T-cell (CAR-T) therapy, presents an opportunity to re-evaluate the predictions of prior cost-effectiveness analyses (CEAs). To conduct a systematic literature review (SLR) of prior CEAs of tisagenlecleucel for pediatric/young adult relapsed or refractory acute lymphoblastic leukemia (r/r ALL) and evaluate the impact of recently available 5-year efficacy data from ELIANA and advances in CAR-T manufacturing in an updated CEA model. OVID MEDLINE/Embase and health technology assessment (HTA) databases were searched for full-text economic evaluations in English reporting cost-effectiveness results for tisagenlecleucel for r/r ALL. Evaluations with publicly reported incremental cost-effectiveness ratios (ICERs) were included in the SLR. Study screening and data abstraction were conducted following PRISMA guidelines. Data extracted included the country/currency, perspective, clinical trial evidence, model structures, long-term efficacy extrapolation approaches (i.e., overall survival [OS]), time horizon, discount rates, and outcomes (i.e., life years [LY], quality-adjusted LY [QALY], and ICERs). The CEA model reported in Wakase et al. was updated using 5-year OS data from ELIANA and the CAR-T infusion rate informed by real-world practice. Sixteen records corresponding to 15 unique studies were included in the SLR (11 publications and 5 HTA reports); all were conducted from the health care system perspective of the respective countries. Most studies found tisagenlecleucel to be cost effective, but all studies' projected 3- and 5-year OS rates for tisagenlecleucel were lower than the observed 3- and 5-year rates, respectively, derived from 5-year ELIANA data. When applying updated OS projections from the most recent ELIANA data cut and higher infusion rates of 92.5% (per the real-world infusion rate)-96.0% (per the manufacturer success rate) to theCEA of Wakase et al., the associated QALYs for tisagenlecleucel increased from 11.6 to 14.6-15.0, andLYs increased from 13.3 to 17.0-17.5. Accordingly, the ICERs for tisagenlecleucel decreased from ¥2,035,071 to ¥1,787,988-¥1,789,048 versus blinatumomab and from ¥2,644,702 to ¥2,257,837-¥2,275,181 versus clofarabine combination therapy in the updated CEA model. Projections at launch of the likely cost effectiveness of tisagenlecleucel appear to have underestimated its ultimate economic value given more recent trial and real-world data. To balance uncertainty in initial valuation with the need to provide access to novel oncology therapies, payers can consider flexible reimbursement policies alongside ongoing assessments as new data emerge.

Tracing the contributions of different nitrate sources associated with submarine groundwater discharge in coastal seawaters off Jeju Island, Korea

We measured the concentrations of dissolved inorganic nutrients and the dual isotopic composition of nitrate (δ15N-NO3− and δ18O-NO3−) in coastal waters off Jeju, a volcanic island in Korea, to trace its main sources. Sampling of seawater and fresh groundwater was conducted in four different coastal areas of Jeju Island: Haengwon (HW), Pyoseon (PS), Ilgwa (IG), and Sagye (SG) in May 2020 and 2021. The significant negative correlations between NO3− and salinity in the four areas indicate that the main source of NO3− is fresh submarine groundwater discharge (FSGD), with the extrapolated fresh groundwater endmember values ranging from 170 to 300 μM (δ15N-NO3−: 4.1–10.8 and δ18O-NO3−: 1.7–6.4). The actual sources of SGD-driven NO3− in these coastal waters were determined using a bi-plot diagram (δ15N-NO3− vs. δ18O-NO3−) and a Bayesian stable isotope mixing model (MixSIAR). The results showed that, besides the background contribution from open-ocean waters, the main sources of NO3− in HW were fertilizer (69 ± 5%) and manure and sewage (24 ± 7%) and those in PS, IG, and SG were manure and sewage (PS: 53 ± 11%, IG: 57 ± 12%, SG: 63 ± 13%) and fertilizer (PS: 27 ± 8%, IG: 24 ± 5%, SG: 22 ± 5%). Our extrapolation approach for NO3− dual isotopes provides a better way to evaluate the main sources of NO3− in coastal waters off volcanic islands where SGD-driven NO3− is significant, but its actual source groundwater cannot be located.

Accelerating the development of genetically engineered cellular therapies: a framework for extrapolating data across related products

BackgroundSignificant advancements have been made in the field of cellular therapy as anti-cancer treatments, with the approval of chimeric antigen receptor (CAR)-T cell therapies and the development of other genetically engineered cellular therapies. CAR-T cell therapies have demonstrated remarkable clinical outcomes in various hematological malignancies, establishing their potential to change the current cancer treatment paradigm. Due to the increasing importance of genetically engineered cellular therapies in the oncology treatment landscape, implementing strategies to expedite development and evidence generation for the next generation of cellular therapy products can have a positive impact on patients. MethodsWe outline a risk-based methodology and assessment aid for the data extrapolation approach across related genetically engineered cellular therapy products. This systematic data extrapolation approach has applicability beyond CAR-T cells and can influence clinical development strategies for a variety of immune therapies such as T cell receptor (TCR) or genetically engineered and other cell-based therapies (e.g., tumor infiltrating lymphocytes, natural killer cells and macrophages). ResultsBy analyzing commonalities in manufacturing processes, clinical trial designs, and regulatory considerations, key learnings were identified. These insights support optimization of the development and regulatory approval of novel cellular therapies. ConclusionsThe field of cellular therapy holds immense promise in safely and effectively treating cancer. The ability to extrapolate data across related products presents opportunities to streamline the development process and accelerate the delivery of novel therapies to patients.