Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “marketing analysis.”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

LOCOMOTIVES - Comprehensive Impact and Cost Assessment Framework of Carbon Lowering Approaches for the US Rail Freight System

The goal of this project is to develop a tool to aid railroads and other stakeholders assess and approach the decarbonization of freight rail operations by identifying new, viable low-carbon energy storage and conversion systems for future locomotive systems and how they should be deployed on the existing US freight rail network. In the first quarter, the project focused on collecting data, establishing a simulation workflow, and engaging industry through the creation of the Industry Advisory Board (IAB). In the second quarter, the project focused on selecting fuel pathways and powertrain technologies, setting performance targets, conducting a techno-economic analyses, and developing the simulation framework that would serve as the backbone of the future toolhead. The third quarter involved developing an industry-oriented interactive dashboard powered by a five-step sequential framework, as well as holding industry advisory board meetings as per the initial technology-to-market plan. In the remaining project quarters, the NUFRIEND dashboard were fine-tuned with the help of IAB member feedback and in-depth scenario analyses were conducted to support the techno-economic analysis of energy sources. Additionally, dashboard documentation, project insights, and open-source code on GitHub were prepared and released. Throughout the project, the team completed testing and analysis of all model components, integrated all initial test scenarios, and conducted stakeholder engagement. Lower-carbon drop-in fuels can be deployed as admixtures and are considered uniform across the network at a desired penetration rate, while hydrogen and battery-electric technology deployment poses a more complex problem as they require significant investments to be made in the siting of refueling/charging facilities and the replacement of locomotive fleets. Thus, strategies for locating and sizing refueling/charging facilities on a railroad’s network to meet their energy demands were developed to inform deployment decisions. To address this challenge, the Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework presents a five-step sequential framework to select O-D paths, locate facilities, reroute flows, size facilities, and evaluate the deployment for alternative energy sources that require locomotive powertrains to be converted and new refueling infrastructure to be deployed. The NUFRIEND Framework is an industry-oriented tool for simulating the deployment of new energy technologies across the US freight rail network. The framework provides a comprehensive network-level optimization and scenario simulation tool for decarbonizing the freight rail sector, addressing the uncertainties surrounding technological developments by supporting sensitivity analyses for different operational and technological parameters through a transparent and flexible input module. It offers practical alternatives to diesel locomotives and can be applied for any railroad considering the specific network structure and freight demand, outputting evaluation metrics for the associated emissions and costs relative to diesel operations. A number of relevant simulation scenarios were run and analyzed for key insights on the value of different alternative technologies for freight rail decarbonization. The project developments and findings have been presented at numerous conferences and events.

08 HYDROGEN↗
DOE OSTI2025

ECE 4396 (Final Report)

In the summer of 2025, I was fortunate enough intern at Sandia National Laboratories in Albuquerque, New Mexico. I was hired into the Southwest Analysis Laboratories for Semiconductor Advancement (SALSA) intern program. In this internship, I applied my knowledge and skills in electrical engineering to conduct hardware failure analysis. I utilized various failure analyze techniques involving the use of Infrared Thermography (IRT) and Laser Scanning Microscopy (LSM) to test different Application-Specific Integrated Circuits (ASIC) chips that are available in the public market.

42 ENGINEERING↗
DOE OSTI2024

Effects of the U.S. inflation reduction act on SMR economics

The U.S. Inflation Reduction Act (IRA) of 2022 provides a wide array of tax credits and other incentives for low-carbon energy. The technology-neutral clean generation production tax credit (PTC) (Section 45Y of the U.S. Internal Revenue Code) and the technology-neutral investment tax credit (ITC) (Section 48E) lower the net cost of new electricity generation projects with zero or negative greenhouse gas emission rates. We evaluate the impact of the IRA legislation—specifically the PTC and ITC—on the cost-competitiveness of small modular reactors (SMRs). We use the Argonne Low-carbon Energy Analysis Framework (A-LEAF) model to calculate the capacity factor of an SMR with a range of hypothetical variable operating and maintenance (O&M) costs in the Electric Reliability Council of Texas (ERCOT) electricity market. We selected ERCOT for market modeling because of its competitive structure, available data, and extensive use in prior literature. We use a discounted cash flow model to calculate the SMR’s net present value based on the market prices and capacity factors from A-LEAF, hypothetical ranges of capital and variable O&M costs, and other input parameters, with or without the IRA tax credits. We determine the SMR owner’s optimal choice of PTC or ITC for the hypothetical ranges of capital and variable O&M costs. We also evaluate potential shifts in the SMR owner’s optimal choice of PTC or ITC based on historical patterns of nuclear capital cost overruns in the United States. We also assess the sensitivity of our results to longer PTC period and electricity prices from the New England market, which tend to be higher than electricity prices in ERCOT. We find that even with the IRA tax credits, only SMRs with low capital and variable O&M costs would be economically feasible in the low-price ERCOT market scenario modeled. A longer PTC period and higher-price market such as New England, however, would significantly expand the economic feasibility of SMRs in the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1982

Medical Products Research

Ventrex Laboratories, Inc. develops, manufactures and markets a line of medical diagnostic assays based on biochemical techniques, in particular immunochemical techniques. Their products are sold worldwide to hospitals and medical laboratories for use in testing blood samples and other biological fluids. Analysis of a patient's body fluids, compared with normal values, aids a physician in confirming or otherwise diagnosing a suspected disease condition. NERAC's rapid information retrieval has provided Ventrex invaluable up-to-date information, and has permitted large scale savings. NERAC's service was particularly important in the development of a new product in the company's Ventre/Sep line, which is used in radioimmunoassays.

Source record↗
NASA NTRSDate not supplied

Creating Formal Characterizations of Routine Contingency Management in Commercial Aviation

The identification, modelling, and analysis of root causes of accidents and incidents dominate conventional safety management approaches. However, the effect of humans’ safety-producing behavior on the overall resilience of the system is often neglected. Additionally, emerging aviation markets are giving rise to concepts of operation, such as urban air mobility and optionally piloted air cargo operations, that are leading to a shift in locus of control between humans and automation. Without an understanding of the human contribution to safety, it is difficult to assess the effects of these novel role allocations on overall system safety. In this work, safety-producing behaviors are identified and abstracted into resilient performance strategies. Production rules that encapsulate these strategies are then generated and classified in the Soar cognitive architecture. The strategies are then applied to a remotely-operated air cargo example to demonstrate how safe learning is facilitated. The learned rules and strategies are then formally verified.

Safety Critical Systems↗
NASA NTRSDate not supplied

Materials Informatics at NASA GRC: Machine Learning Surrogate Modeling, Data Management, and Integrated Toolsets for Establishing/Maintaining the Digital Thread

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which heavily depend on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results that is findable and usable, along with integrated, efficient toolsets for effectively passing information across various length and time scales across such models. At the NASA Glenn Research Center under the Transformational Tools and Technologies Project, significant recent efforts have been directed towards establishing the required cyberinfrastructure to enable optimized ICME processes and the design of “fit-for-purpose” materials to achieve the goals outlined in the NASA Vision 2040 report. Such efforts include development of multiscale physics-based material models, which can be used to train highly efficient surrogate machine learning models, development of best practices and infrastructure for effective, traceable materials information management, and development of toolsets that integrate with physics-based codes, machine learning models, and an information management system to enable high throughput of materials data collection and analysis, establishment of digital twins and the digital thread, and automation of the ICME design process for material optimization.

Machine Learning↗
DOE OSTI2022

Large-Scale Simulation of Regional Demand Flexibility Implementation and Customer Economic Impact

The Distribution System Operator with Transactive (DSO+T) study conducted a large-scale simulation of over 60,000 customers in a region the size of Texas to demonstrate the effective coordination of distributed energy resources (DERs) in commercial and residential buildings. The integrated simulation included both the bulk (wholesale generation and transmission) and distribution systems. The DERs (including batteries, electric vehicles, air conditioning, and water heaters) participated in a transactive energy retail market that was integrated into an existing competitive wholesale market. The engineering and economic performance of the resulting demand flexibility was evaluated over annual simulations for both moderate and high renewable generation scenarios. A detailed parametric cost model was developed to enable detailed economic analysis of key stakeholders. In addition, fixed and dynamic customer tariffs were designed and applied to the customer population. This allowed the impact on annual customer bills to be analyzed for various building types (residential versus commercial; single- versus multi-family). This paper presents results showing the relative flexibility of batteries, electric vehicles, and building loads throughout the year and under different renewable scenarios. This feeds a detailed breakdown of the impact this flexibility has on the operating costs of the grid and the resulting net economic benefit. Finally, the study showed that practically all customer classes (including non-participating customers) save money under the proposed demand flexibility scheme. The study found overall net annual economic savings of $3.3-5.0B for a region the size of Texas equating to average customer bill savings of 10-16%.

Reeve, Hayden M.↗
NASA NTRSDate not supplied

SimUAM: A Comprehensive Microsimulation Toolchain to Evaluate the Impact of Urban Air Mobility in Metropolitan Areas

Over the past several years, Urban Air Mobility (UAM) has galvanized enthusiasm from investors and researchers, marrying expertise in aircraft design, transportation, logistics, artificial intelligence, battery chemistry, and broader policymaking. However, two significant questions remain unexplored: (1) What is the value of UAM in a region’s transportation network? and (2) How can UAM be effectively deployed to realize and maximize this value to all stakeholders, including riders and local economies? To adequately understand the value proposition of UAM for metropolitan areas, the authors develop a holistic multi-modal toolchain, SimUAM, to model and simulate UAM and its impacts on travel behavior. This toolchain has several components: (1) Microsimulation Analysis for Network Traffic Assignment (MANTA): A fast, high-fidelity regional-scale traffic microsimulator, (2) VertiSim: Agranular, discrete-event vertiport and pedestrian simulator, (3) Flexible Engine for Fast-time Evaluation of Flight Environments (Fe3): A high-fidelity, trajectory-based aerial microsimulation. SimUAM, rooted in granular, GPU-based microsimulation, models millions of trips and their movements in the street network and in the air, producing interpretable and actionable performance metrics for UAM designs and deployments. Once the ground-air interface is modeled, the authors find that the market for UAM decreases across all network designs relative to models with static assumptions about transfer times. However, significant improvements can be made to balance the demand and optimize the networks for transfer time, likely increasing the number of benefited trips. The modularity, extensibility, and speed of the platform will allow for rapid scenario planning and sensitivity analysis, effectively acting as a detailed performance assessment tool.

urban air mobility↗
DOE OSTI2020

Montana Community-Scale Solar Strategy Project (Final Technical Report)

Grid-tied solar photovoltaic (PV) development began gradually in Montana, initially taking root in the early 2000s, largely in the form of small residential systems, rarely larger than two kilowatts. Utility support for solar technology through public benefit funding triggered the installation of the state’s first solar PV installations on community-owned facilities including schools, libraries, fire stations and local government buildings in the mid-2000s. By 2016, solar PV installations totaled approximately 28 megawatts of installed capacity, generating less than 0.05% of electricity sold in Montana. Montana’s solar fleet in 2016 included 17 megawatts of utility-scale solar farms, and three shared solar projects developed by rural electric cooperatives that ranged from 25 to 50 kilowatts in size. However, with only 3,000 households utilizing solar PV, either through an on-site installation or participation in a shared solar project, the potential benefits and reach of solar technology was limited. The Montana Solar Community Project (MSCP) was developed by the Montana Energy Office (MEO) at the Department of Environmental Quality in order to implement a “community-scale” solar energy strategy for the state that would cost-effectively expand Montanans’ access to solar PV. This project defines three types of community-scale solar projects: shared solar, community-sited solar, and group purchasing programs. The MSCP project conducted research and stakeholder engagement that helped to inform and develop model community-scale initiatives and an overarching community-scale solar strategy for Montana. Specifically, the project included a solar market assessment, stakeholder meetings across Montana, development of a menu of community-scale solar options, a study of solar potential on schools, community solar market research, and community-scale project implementation support. MSCP activities were successful in implementing solar projects and supporting solar development in communities across Montana. Project activities realized 280 kW of newly installed solar from two solarize programs, a shared solar array at a rural electric cooperative, and a solar array atop a Bozeman high school. The community-scale project implementation support and Solar on Schools components of the project resulted directly in the analysis and design of nearly 2 MW of distributed solar systems across the state. The Solar on Schools analysis alone produced solar designs for 25 schools, representing 920.8 kW of solar potential. Many of the deliverables from this project provide guidance on future development of community-scale solar in Montana, and resulted in a number of important conclusions about community-scale solar in Montana. There is strong interest in community-scale solar in Montana, as noted during stakeholder meetings, in response to solar market research surveys, and as seen by applications for implementation support grants provided by MEO. Project activities also suggest that Montanans demonstrated a desire and need for more information about solar energy technology, solar financing options, and the state’s policy and regulatory framework. Additionally, the project found that schools will be key partners for future community-scale solar programs. In addition to being excellent hosts for solar arrays, schools have the opportunity to use the installation of a solar array to develop educational materials and curricula to educate Montana’s future leaders about these technologies, which can be shared and replicated across the state. Project activities laid a clear path forward for solar energy in Montana. Certain activities have concrete next steps. For example, numerous solar arrays were designed for schools and other public buildings around the state. Many of those designs will be used as soon as funding becomes available. Other activities have less developed next steps, though lay the foundation for future work. The solar market assessment, solar market research, and community stakeholder meetings are good examples. These documents can be used as stepping stones to increase renewable energy education, increase stakeholder outreach, and support communities as they seek to develop community-scale solar projects.

14 SOLAR ENERGY↗
DOE OSTI2022

Light-Duty Vehicle Choice Modeling and Transportation Decarbonization Analysis

Light-duty vehicles are the biggest contributor of carbon emissions in the transportation sector. This project uses the Automotive Deployment Options Projection Tool (ADOPT) to explore pathways to light-duty decarbonization through technology improvements being researched by the Department of Energies Vehicle Technology Office. It includes sensitivities to market conditions, such as proposed vehicle purchase incentives, and changes to the Corporate Average Fuel Economy and Greenhouse Gas regulations. The results suggest that achieving the Vehicle Technology Office's research goals will lead to one third of the annual carbon emissions by 2050, primarily through vehicle electrification.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2026

Forest residue harvest optimization: spanning the bridge between plant biology and biorefinery performance

Forestry residues have immense potential as alternative feedstocks to petroleum, yet their inherent complexity remains a major challenge to widespread use. Pairing the temporal rhythms of plant biology with biorefinery performance is critical to industrial-scale biorefinery development. Here, we provide the first report of a techno-economic analysis (TEA) and life cycle assessment (LCA) for a model integrated reductive catalytic fractionation (RCF)–molten salt hydrolysis process for forestry residues varying in tree part, species, and phenophase. All forestry residues resulted in net-negative greenhouse gas (GHG) emissions vs. comparable petroleum feedstocks, with GHG emissions potentially reduced >4.0× through composition-based feedstock selection (e.g., harvesting American beech bark in spring vs. summer). Moreover, American beech twigs/branchlets and bark in leafed and emergence phenophases, respectively, had 7.9× lower predicted phenolic minimum selling prices (MSPs) vs. other feedstocks and MSPs within the current global phenolic market range. Hemicellulose content and RCF yield emerged as key parameters impacting GHG emissions and biorefinery revenue, identifying hardwood twigs/branchlets in the leafed phenophase as optimal biofeedstocks. Biorefinery expenses were dominated by purchased equipment, raw materials, and utility costs, highlighting essential areas for future study. Notably, RCF reactor pressures drove 85–90% of equipment costs, but sensitivity analysis revealed that decreasing the pressure 20% could reduce the phenol MSP 4-fold. Structural carbohydrate dynamics were also investigated using a two-step acid hydrolysis method to resolve tissue- and species-level patterns in biomass composition throughout the year to enable harvest optimization based on TEA/LCA findings. Ultimately, elucidating the impact of biofeedstock dynamics on biorefinery performance enables harvest optimization, informed engineering design, and progress towards an expanded bioeconomy.

Shapiro, Alison J. [University of Delaware, Newark↗
DOE OSTI2021

NREL’s Wind Turbine Drivetrain Condition Monitoring and Wind Plant Operation and Maintenance Research During the 2010s: A US Land-Based Perspective

The wind industry has seen tremendous growth during the past two decades, with the global cumulative installation capacity reaching more than 650 gigawatts by the end of 2019. Despite performance and reliability improvements of utility-scale wind turbines over the years, the industry still experiences premature component failures, leading to increased operation and maintenance (O&M) costs. Among various turbine components, gearboxes—and, more broadly, drivetrains—have shown to be costly to maintain throughout the design life of a wind turbine. The problem of premature component failure is industry wide. As early as 2007, the US Department of Energy (DOE) started to address this challenge through the National Renewable Energy Laboratory’s (NREL’s) reliability initiative that first focused on gearboxes and more recently expanded to entire drivetrains. The wind turbine drivetrain condition monitoring and wind plant O&M research that is the subject of this paper is part of the NREL initiative and includes a few research and development (R&D) activities conducted during the 2010s. These activities included technology evaluation during the first few years; novel monitoring technique investigation (specifically, compact filter analysis) during the middle years; and data and physics domain modeling for fault detection and prediction in recent years. A high-level summary of these activities is provided in this paper along with some key observations from each activity. Most of the work discussed has been published and can be referred to for more information. They reflect the expected evolution of wind turbine condition monitoring and O&M in the US market—primarily, a land-based perspective. In addition, we have identified several R&D opportunities that can be picked up by the research community to help industry advance in related areas, making wind power more cost competitive in the future.

17 WIND ENERGY↗
DOE OSTI2025

A critical review and meta-analysis of energy demand, carbon footprint, and other environmental impacts from carbon fiber manufacturing

The demand for carbon fibers and carbon fiber-reinforced polymers (CFRPs) is rapidly growing due to their outstanding mechanical properties and potential to enhance sustainability, particularly for lightweighting applications. However, carbon fibers are typically produced from fossil-based feedstocks, involve energy-intensive processes, and have limited options for sustainable end-of-life management or circularity. Despite these challenges, the energy demand and lifecycle environmental implications of their production remain poorly understood. Here, we conduct a critical literature review and meta-analysis of carbon fiber manufacturing, revealing significant variations in reported energy demand, carbon footprint, and lifecycle inventory data. Our analysis makes two novel contributions. First, we identify key underlying factors driving these variations. Second, we highlight that carbon fiber, far from being a homogeneous product, has grades varying substantially in mechanical properties, end-use markets, energy intensity of manufacturing processes, and therefore environmental impacts—an aspect often underrepresented in life cycle assessments. We assert that current data are insufficient for reliably evaluating environmental impacts, posing a risk of misleading decision-making. Addressing this gap requires new lifecycle inventory datasets clearly incorporating carbon fiber heterogeneity and key influencing factors identified in this study. Additionally, we propose actionable recommendations, including a checklist, to advance sustainability in the carbon fiber sector.

CED↗
DOE OSTI2024

Task-oriented machine learning surrogates for tipping points of agent-based models

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Lighting Technology Energy Solutions (LiTES) (Final Technical Report)

The Lighting Technologies Energy Solutions (LiTES) Program Final Technical Report is a collaborative effort between NextEnergy, Consumers Energy, DTE Energy (DTE) and the Detroit Joint Apprentice Training Center (DJATC) to evaluate customer and contractor experiences deploying advanced/networked lighting controls systems (ALC/NLC). The LiTES Program, funded by the US Department of Energy (DOE), sought to reduce energy use in small and medium commercial buildings (SMCB) by accelerating the adoption of ALC/NLC through contractor training and technology deployment. Leveraging recommendations already outlined by the Design Lights Consortium Commercial Advanced Lighting Controls (DLC CALC) project, NextEnergy led an effort to train contractors, evaluate the experience of ALC/NLC demonstration projects, identify opportunities to reduce market barriers, and accelerate the increased adoption of ALC/NLC technologies within SMCB. The LiTES Program defined SMCB as commercial buildings under 100,000 square feet. The LiTES Program efforts also included evaluating current ALC/NLC utility incentives, piloting ALC/NLC incentives specific to SMCB, and identifying opportunities to better align utility incentives with current ALC/NLC technology to support accelerating the adoption of ALC/NLC in SMCB. This report shares the survey results and M&V data findings, including energy savings, market barriers, experience of installations, impact of incentives/financing programs and provides recommendations for the increased deployment of ALC/NLC systems in small and medium commercial buildings. Utility survey results, incentive alternatives and recommendations, including a cost benefit analysis outlining energy and economic impacts of the provided ALC/NLC incentives on SMCB projects are also provided.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Electric Cooking Equipment

Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models over the past three years, the objective of this work is to produce national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility end-use load shapes (electricity, gas, propane, or fuel oil) that cover most of the high-impact, market-ready (or nearly market-ready) measures. “Measures” refers to energy efficiency variables that can be applied to buildings during modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Console Water-to-Air Geothermal Heat Pump

Executive Summary Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models over the past several years, the objective of this work is to produce national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility end-use load shapes (electricity, gas, propane, or fuel oil) that cover most of the high-impact, market-ready (or nearly market-ready) measures. An end-use savings shape is the difference in energy consumption between a baseline building and a building with an energy efficiency, electrification, or demand flexibility measure applied. It results in a time series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. This measure models the conversion of an existing heating, ventilating, and air-conditioning (HVAC) system to a series of “console” water-to-air heat pumps served by a ground heat exchanger. Console water-to-air geothermal heat pumps (GHP) are all-in-one packages that have no or minimal ductwork and serve individual spaces. Properly designed ground heat exchanger-coupled systems can offer benefits in energy efficiency relative to “conventional” HVAC systems, as well as facilitating beneficial electrification. Console GHPs can be coupled to a ground loop on the source side and can directly replace electric baseboard heaters or air-source packaged terminal heat pumps. Console GHPs can also bring in outdoor air for ventilation. This measure will be referred to throughout the document as the “Console GHP” upgrade.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

NMR Spectroscopy for Protein Higher Order Structure Similarity Assessment in Formulated Drug Products

Peptide and protein drug molecules fold into higher order structures (HOS) in formulation and these folded structures are often critical for drug efficacy and safety. Generic or biosimilar drug products (DPs) need to show similar HOS to the reference product. The solution NMR spectroscopy is a non-invasive, chemically and structurally specific analytical method that is ideal for characterizing protein therapeutics in formulation. However, only limited NMR studies have been performed directly on marketed DPs and questions remain on how to quantitively define similarity. Here, NMR spectra were collected on marketed peptide and protein DPs, including calcitonin-salmon, liraglutide, teriparatide, exenatide, insulin glargine and rituximab. The 1D 1 H spectral pattern readily revealed protein HOS heterogeneity, exchange and oligomerization in the different formulations. Principal component analysis (PCA) applied to two rituximab DPs showed consistent results with the previously demonstrated similarity metrics of Mahalanobis distance (D M ) of 3.3. The 2D 1 H- 13 C HSQC spectral comparison of insulin glargine DPs provided similarity metrics for chemical shift difference (Δδ) and methyl peak profile, i.e., 4 ppb for 1 H, 15 ppb for 13 C and 98% peaks with equivalent peak height. Finally, 2D 1 H- 15 N sofast HMQC was demonstrated as a sensitive method for comparison of small protein HOS. The application of NMR procedures and chemometric analysis on therapeutic proteins offer quantitative similarity assessments of DPs with practically achievable similarity metrics.

59 BASIC BIOLOGICAL SCIENCES↗