Determining the structure of the bacterial voltage-gated sodium channel NaChBac embedded in liposomes by cryo electron tomography and subtomogram averaging.

Voltage-gated sodium channels shape action potentials that propagate signals along cells. When the membrane potential reaches a certain threshold, the channels open and allow sodium ions to flow through the membrane depolarizing it, followed by the deactivation of the channels. Opening and closing of the channels is important for cellular signalling and regulates various physiological processes in muscles, heart and brain. Mechanistic insights into the voltage-gated channels are difficult to achieve as the proteins are typically extracted from membranes for structural analysis which results in the loss of the transmembrane potential that regulates their activity. Here, we report the structural analysis of a bacterial voltage-gated sodium channel, NaChBac, reconstituted in liposomes under an electrochemical gradient by cryo electron tomography and subtomogram averaging. We show that the small channel, most of the residues of which are embedded in the membrane, can be localized using a genetically fused GFP. GFP can aid the initial alignment to an average resulting in a correct structure, but does not help for the final refinement. At a moderate resolution of ˜16 Å the structure of NaChBac in an unrestricted membrane bilayer is 10% wider than the structure of the purified protein previously solved in nanodiscs, suggesting the potential movement of the peripheral voltage-sensing domains. Our study explores the limits of structural analysis of membrane proteins in membranes.

Discovery of small-molecule protein stabilizers toward exogenous alpha-l-iduronidase to reduce the accumulated heparan sulfate in mucopolysaccharidosis type I cells.

Synthesis of a series of l-iduronic acid (IdoA)- and imino-IdoA-typed C-glycosides for modulating α-l-iduronidase (IDUA) activity is described. In an enzyme inhibition study, IdoA-typed C-glycosides were more potent than imino-IdoA analogs, with the most potent IdoA-typed C-glycoside 27c showing an IC50 value of 1 µM. On the other hand, co-treatment of 12 with rh-α-IDUA in mucopolysaccharidosis type I (MPS I) fibroblasts exhibited a nearly 3-fold increase of the IDUA activity, resulting in a clear reduction of the accumulated heparan sulfate (HS) compared to the exogenous enzyme treatment alone. This is the first report of small molecules facilitating IDUA stabilization, enhancing enzyme activity, and reducing accumulated HS in MPS I cell-based assays, which reveals that small molecules as rh-α-IDUA stabilizers to improve enzyme replacement therapy (ERT) efficacy toward MPS I is feasible and promising.

Empirical nitrogen and sulfur critical loads of U.S. tree species and their uncertainties with machine learning.

Critical loads (CLs) of atmospheric deposition for nitrogen (N) and sulfur (S) are used to support decision making related to air regulation and land management. Frequently, CLs are calculated using empirical methods, and the certainty of the results depends on accurate representation of underlying ecological processes. Machine learning (ML) models perform well in empirical modeling of processes with non-linear characteristics and significant variable interactions. We used bootstrap ensemble ML methods to develop CL estimates and assess uncertainties of CLs for the growth and survival of 108 tree species in the conterminous United States. We trained ML models to predict tree growth and survival and characterize the relationship between deposition and tree species response. Using four statistical methods, we quantified the uncertainty of CLs in 95 % confidence intervals (CI). At the lower bound of the CL uncertainty estimate, 80 % or more of tree species have been impacted by nitrogen deposition exceeding a CL for tree survival over >50 % of the species range, while at the upper bound the percentage is much lower (<20 % of tree species impacted across >60 % of the species range). Our analysis shows that bootstrap ensemble ML can be effectively used to quantify critical loads and their uncertainties. The range of the uncertainty we calculated is sufficiently large to warrant consideration in management and regulatory decision making with respect to atmospheric deposition.

Daily Associations of Air Pollution and Pediatric Asthma Risk Using the Biomedical REAI-Time Health Evaluation (BREATHE) Kit.

Background: Exposure to air pollution is associated with acute pediatric asthma exacerbations, including reduced lung function, rescue medication usage, and increased symptoms; however, most studies are limited in investigating longitudinal changes in these acute effects. This study aims to investigate the effects of daily air pollution exposure on acute pediatric asthma exacerbation risk using a repeated-measures design. Methods: We conducted a panel study of 40 children aged 8−16 years with moderate-to-severe asthma. We deployed the Biomedical REAI-Time Health Evaluation (BREATHE) Kit developed in the Los Angeles PRISMS Center to continuously monitor personal exposure to particulate matter of aerodynamic diameter < 2.5 µm (PM2.5), relative humidity and temperature, geolocation (GPS), and asthma outcomes including lung function, medication use, and symptoms for 14 days. Hourly ambient (PM2.5, nitrogen dioxide (NO2), ozone (O3)) and traffic-related (nitrogen oxides (NOx) and PM2.5) air pollution exposures were modeled based on location. We used mixed-effects models to examine the association of same day and lagged (up to 2 days) exposures with daily changes in % predicted forced expiratory volume in 1 s (FEV1) and % predicted peak expiratory flow (PEF), count of rescue inhaler puffs, and symptoms. Results: Participants were on average 12.0 years old (range: 8.4−16.8) with mean (SD) morning %predicted FEV1 of 67.9% (17.3%) and PEF of 69.1% (18.4%) and 1.4 (3.5) puffs per day of rescue inhaler use. Participants reported chest tightness, wheeze, trouble breathing, and cough symptoms on 36.4%, 17.5%, 32.3%, and 42.9%, respectively (n = 217 person-days). One SD increase in previous day O3 exposure was associated with reduced morning (beta [95% CI]: −4.11 [−6.86, −1.36]), evening (−2.65 [−5.19, −0.10]) and daily average %predicted FEV1 (−3.45 [−6.42, −0.47]). Daily (lag 0) exposure to traffic-related PM2.5 exposure was associated with reduced morning %predicted PEF (−3.97 [−7.69, −0.26]) and greater odds of "feeling scared of trouble breathing" symptom (odds ratio [95% CI]: 1.83 [1.03, 3.24]). Exposure to ambient O3, NOx, and NO was significantly associated with increased rescue inhaler use (rate ratio [95% CI]: O3 1.52 [1.02, 2.27], NOx 1.61 [1.23, 2.11], NO 1.80 [1.37, 2.35]). Conclusions: We found significant associations of air pollution exposure with lung function, rescue inhaler use, and "feeling scared of trouble breathing." Our study demonstrates the potential of informatics and wearable sensor technologies at collecting highly resolved, contextual, and personal exposure data for understanding acute pediatric asthma triggers.

KAHRP dynamically relocalizes to remodeled actin junctions and associates with knob spirals in Plasmodium falciparum-infected erythrocytes.

The knob-associated histidine-rich protein (KAHRP) plays a pivotal role in the pathophysiology of Plasmodium falciparum malaria by forming membrane protrusions in infected erythrocytes, which anchor parasite-encoded adhesins to the membrane skeleton. The resulting sequestration of parasitized erythrocytes in the microvasculature leads to severe disease. Despite KAHRP being an important virulence factor, its physical location within the membrane skeleton is still debated, as is its function in knob formation. Here, we show by super-resolution microscopy that KAHRP initially associates with various skeletal components, including ankyrin bridges, but eventually colocalizes with remnant actin junctions. We further present a 35 Å map of the spiral scaffold underlying knobs and show that a KAHRP-targeting nanoprobe binds close to the spiral scaffold. Single-molecule localization microscopy detected ~60 KAHRP molecules/knob. We propose a dynamic model of KAHRP organization and a function of KAHRP in attaching other factors to the spiral scaffold.

Structure of the merozoite surface protein 1 from Plasmodium falciparum.

The merozoite surface protein 1 (MSP-1) is the most abundant protein on the surface of the erythrocyte-invading Plasmodium merozoite, the causative agent of malaria. MSP-1 is essential for merozoite formation, entry into and escape from erythrocytes, and is a promising vaccine candidate. Here, we present monomeric and dimeric structures of full-length MSP-1. MSP-1 adopts an unusual fold with a large central cavity. Its fold includes several coiled-coils and shows structural homology to proteins associated with membrane and cytoskeleton interactions. MSP-1 formed dimers through these domains in a concentration-dependent manner. Dimerization is affected by the presence of the erythrocyte cytoskeleton protein spectrin, which may compete for the dimerization interface. Our work provides structural insights into the possible mode of interaction of MSP-1 with erythrocytes and establishes a framework for future investigations into the role of MSP-1 in Plasmodium infection and immunity.

Air quality and source apportionment modeling of year 2017 ozone episodes in Albuquerque/Bernalillo County, New Mexico.

Albuquerque/Bernalillo County, New Mexico, is currently in attainment of the 2015 National Ambient Air Quality Standard (NAAQS) for ozone (70 ppb), but its ozone design values have increased in recent years. Air quality and source apportionment modeling with the Comprehensive Air Quality Model with Extensions (CAMx) was conducted for Albuquerque/Bernalillo County to develop a refined understanding of ozone source apportionment in the region, estimate ozone concentrations in the year 2025 based on projected changes in anthropogenic emissions, and evaluate the sensitivity of future ozone concentrations to various changes in local and non-local emissions. The study focused on two ozone episodes during June and July 2017 when 8-hr average ozone concentrations were greater than 70 ppb. Based on the modeling results, ozone during the June 2017 episode was found to be driven largely by contributions from non-local and regional emissions, whereas ozone during the July 2017 episode was driven more strongly by local emissions from within Albuquerque/Bernalillo County. On high ozone days, anthropogenic emissions from within Albuquerque/Bernalillo County contributed between 8% and 19% (6-14 ppb) of total ozone. Half of this local ozone contribution was from on-road mobile sources. Fire emissions contributed as much as 2 ppb of ozone on a given day. Contributions from large power plants in New Mexico were as large as 1 ppb on a given day but less than 0.5 ppb on most days. Modeled ozone concentrations in Albuquerque/Bernalillo County were also sensitive to emissions from oil and gas emissions in New Mexico. If projected emission reductions by 2025 materialize, these reductions could reduce future peak 8-hr average ozone concentrations by as much as 3-4% compared to 2017 values. Implications: The results of this study have important implications for air quality management in Albuquerque/Bernalillo County. Ozone in Albuquerque/Bernalillo County is the result of local and non-local emissions, is impacted by wildfires, and is sensitive to statewide oil and gas emissions. The magnitude of modeled contributions from anthropogenic emissions within Albuquerque/Bernalillo County is strongly influenced by meteorological conditions, transport pathways, and the presence of wildfire. This modeling is important for understanding the potential effectiveness of local emission controls in Albuquerque/Bernalillo County, and can serve as a basis for testing future regional and local emission control options.

Fine-Scale Modeling of Individual Exposures to Ambient PM2.5, EC, NOx, CO for the Coronary Artery Disease and Environmental Exposure (CADEE) Study.

Air pollution epidemiological studies often use outdoor concentrations from central-site monitors as exposure surrogates, which can induce measurement error. The goal of this study was to improve exposure assessments of ambient fine particulate matter (PM2.5), elemental carbon (EC), nitrogen oxides (NOx), and carbon monoxide (CO) for a repeated measurements study with 15 individuals with coronary artery disease in central North Carolina called the Coronary Artery Disease and Environmental Exposure (CADEE) Study. We developed a fine-scale exposure modeling approach to determine five tiers of individual-level exposure metrics for PM2.5, EC, NOx, CO using outdoor concentrations, on-road vehicle emissions, weather, home building characteristics, time-locations, and time-activities. We linked an urban-scale air quality model, residential air exchange rate model, building infiltration model, global positioning system (GPS)-based microenvironment model, and accelerometer-based inhaled ventilation model to determine residential outdoor concentrations (Cout_home, Tier 1), residential indoor concentrations (Cin_home, Tier 2), personal outdoor concentrations (Cout_personal, Tier 3), exposures (E, Tier 4), and inhaled doses (D, Tier 5). We applied the fine-scale exposure model to determine daily 24-h average PM2.5, EC, NOx, CO exposure metrics (Tiers 1-5) for 720 participant-days across the 25 months of CADEE. Daily modeled metrics showed considerable temporal and home-to-home variability of Cout_home and Cin_home (Tiers 1-2) and person-to-person variability of Cout_personal, E, and D (Tiers 3-5). Our study demonstrates the ability to apply an urban-scale air quality model with an individual-level exposure model to determine multiple tiers of exposure metrics for an epidemiological study, in support of improving health risk assessments.

A novel co-processing method to manufacture an API for extended release formulation via formation of agglomerates of active ingredient and hydroxypropyl methylcellulose during crystallization.

A novel process for generating agglomerates of active pharmaceutical ingredient (API) and polymer by swelling the polymer in a water/organic mixture has been developed to address formulation issues resulting from a water sensitive, high drug load API with poor powder properties. Initially, the API is dissolved in water, following which hydroxypropyl methylcellulose (HPMC) is added, resulting in the imbibing of water, along with the dissolved API, into the HPMC matrix. The addition of acetone and isopropyl acetate (anti-solvents) then causes the API to crystallize inside and on the surface of HPMC agglomerates. The process was scaled up to 20 kg scale. The agglomerates of API and HPMC generated by this process are ∼350 µm diameter, robust, and have significantly better flow than the API as measured by Erweka flow testing. These agglomerates exhibit improved bulk density, acceptable chemical stability, and high compressibility. The agglomerates process well through roller compaction and tableting, with no flow or sticking issues. This process is potentially adaptable to other APIs with similar attributes.

A combinatorial approach towards the synthesis of non-hydrolysable triazole-iduronic acid hybrid inhibitors of human α-l-iduronidase: discovery of enzyme stabilizers for the potential treatment of MPSI.

Preparation of substituent-diverse, triazole-iduronic acid hybrid molecules by click reaction of an azido iduronic acid derivative with randomly chosen alkynes is described. Library members were screened for their ability to inhibit α-l-iduronidase, and hit molecules and analogues were then investigated for their ability to stabilize rh-α-IDUA in a thermal denaturation study. This work resulted in the discovery of the first small molecules that can be used to stabilize exogenous rh-α-IDUA protein in vitro.

Finely Resolved On-Road PM2.5 and Estimated Premature Mortality in Central North Carolina.

To quantify the on-road PM2.5 -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM2.5 -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM2.5 concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM2.5 -related premature mortality than CMAQ. The major difference is from the primary on-road PM2.5 where the hybrid approach estimated 2.5 times more primary on-road PM2.5 -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM2.5 and suggesting that previous studies may have underestimated premature mortality due to PM2.5 from traffic-related emissions.

Comparison of Highly Resolved Model-Based Exposure Metrics for Traffic-Related Air Pollutants to Support Environmental Health Studies.

Human exposure to air pollution in many studies is represented by ambient concentrations from space-time kriging of observed values. Space-time kriging techniques based on a limited number of ambient monitors may fail to capture the concentration from local sources. Further, because people spend more time indoors, using ambient concentration to represent exposure may cause error. To quantify the associated exposure error, we computed a series of six different hourly-based exposure metrics at 16,095 Census blocks of three Counties in North Carolina for CO, NO(x), PM(2.5), and elemental carbon (EC) during 2012. These metrics include ambient background concentration from space-time ordinary kriging (STOK), ambient on-road concentration from the Research LINE source dispersion model (R-LINE), a hybrid concentration combining STOK and R-LINE, and their associated indoor concentrations from an indoor infiltration mass balance model. Using a hybrid-based indoor concentration as the standard, the comparison showed that outdoor STOK metrics yielded large error at both population (67% to 93%) and individual level (average bias between -10% to 95%). For pollutants with significant contribution from on-road emission (EC and NO(x)), the on-road based indoor metric performs the best at the population level (error less than 52%). At the individual level, however, the STOK-based indoor concentration performs the best (average bias below 30%). For PM(2.5), due to the relatively low contribution from on-road emission (7%), STOK-based indoor metric performs the best at both population (error below 40%) and individual level (error below 25%). The results of the study will help future epidemiology studies to select appropriate exposure metric and reduce potential bias in exposure characterization.

A modeling framework for characterizing near-road air pollutant concentration at community scales.

In this study, we combine information from transportation network, traffic emissions, and dispersion model to develop a framework to inform exposure estimates for traffic-related air pollutants (TRAPs) with a high spatial resolution. A Research LINE source dispersion model (R-LINE) is used to model multiple TRAPs from roadways at Census-block level for two U.S. regions. We used a novel Space/Time Ordinary Kriging (STOK) approach that uses data from monitoring networks to provide urban background concentrations. To reduce the computational burden, we developed and applied the METeorologically-weighted Averaging for Risk and Exposure (METARE) approach with R-LINE, where a set of selected meteorological data and annual average daily traffic (AADT) are used to obtain annual averages. Compared with explicit modeling, using METARE reduces CPU-time by 88-fold (46.8h versus 32min), while still retaining accuracy of exposure estimates. We show two examples in the Piedmont region in North Carolina (~105,000 receptors) and Portland, Maine (~7000 receptors) to characterize near-road air quality. Concentrations for NOx, PM2.5, and benzene in Portland drop by over 40% within 200m away from the roadway. The concentration drop in North Carolina is less than that in Portland, as previously shown in an observation-based study, showing the robustness of our approach. Heavy-duty diesel vehicles (HDDV) contribute over 55% of NOx and PM2.5 near interstate highways, while light-duty gasoline vehicles (LDGV) contribute over 50% of benzene to urban areas where multiple roadways intersect. Normalized mean error (NME) between explicit modeling and METARE in Portland ranges from 12.6 to 14.5% and normalized mean bias (NMB) ranges from -12.9 to -11.2%. When considering a static emission rate (i.e. the emission does not have temporal variability), both NME and NMB improved (10.5% and -9.5%). Modeled concentrations in Detroit, Michigan at an array of near-road monitors are within a factor of 2 of observed values for CO but not NOx.

Evaluating the performance of the horizontal radial plume mapping technique for locating multiple plumes.

This paper evaluated the feasibility of using the horizontal radial plume mapping (HRPM) technique to locate multiple emission sources via computational simulation. Seventy-two test maps, each having two Gaussian distributions, were generated in a two-dimensional domain. The HRPM technique with the non-negative least square (NNLS) algorithm was then applied to reconstruct the plumes, assuming a nine-beam scanning beam geometry. The NNLS algorithm successfully reconstructed the source locations of 68 of the 72 test maps. However, when one of the plumes was near the origin, the NNLS did not always identify the peak locations correctly. Furthermore, when the two plumes were spaced closely, the NNLS tended to reconstruct a wide plume covering both plumes instead of separating them due to the resolution limitation of the current nine-beam geometry. In the sensitivity analysis, five sets of random error (1%, 5%, 10%, 20%, and 30%) were added in the path-integrated concentration (PIC) from the 72 test maps, and thus, an additional 360 reconstructions were implemented. Robust results were obtained when the noise added was less than 20%. The results generally support the implementation of the NNLS algorithm in the HRPM technique as described in the US. Environmental Agency (EPA) Other Test Method 10 (OTM-10).

An evaluation of process parameters to improve coating efficiency of an active tablet film-coating process.

Effects of material and manufacturing process parameters on the efficiency of an aqueous active tablet film-coating process in a perforated pan coater were evaluated. Twenty-four batches representing various core tablet weights, sizes, and shapes were coated at the 350-500 kg scale. The coating process efficiency, defined as the ratio of the amount of active deposited on tablet cores to the amount of active sprayed, ranged from 86 to 99%. Droplet size and velocity of the coating spray were important for an efficient coating process. Factors governing them such as high ratios of the suspension spray rate to atomization air flow rate, suspension spray rate to pattern air flow rate, or atomization air flow rate to pattern air flow rate improved the coating efficiency. Computational fluid dynamics modeling of the droplets showed that reducing the fraction of the smaller droplets, especially those smaller than 10 µm, resulted in a marked improvement in the coating efficiency. Other material and process variables such as coating suspension solids concentration, pan speed, tablet velocity, exhaust air temperature, and the length of coating time did not affect the coating efficiency profoundly over the ranges examined here.

Use of enthalpy and Gibbs free energy to evaluate the risk of amorphous formation.

The control of crystalline and amorphous phases is important during the development of a new drug candidate. Our approach begins with an understanding of the thermodynamics of these two phases. We have developed a quantitative yet practical work flow consisting of three steps towards the analysis of the risk of amorphous material formation. First, we derive the thermodynamic equations to calculate the enthalpy, Gibbs free energy, and the solubility of each phase and their differences as a function of temperature. The enthalpy for each crystalline drug substance at its melting point is selected as the reference state to enable a consistent approach for all analysis. Second, we use data from DSC measurements and the derived thermodynamic equations to construct the enthalpy, Gibbs free energy and solubility diagrams so as to compare the characteristics of these two phases. Finally, we use the results of these calculations to evaluate the potential risk of crystalline-to-amorphous phase conversion during processing of either the drug substance or the drug product. In addition, the impact of amorphous formation on solubility is evaluated. Two drug candidates are used to illustrate this workflow for risk analysis.

Modeling of pan coating processes: Prediction of tablet content uniformity and determination of critical process parameters.

We developed an engineering model for predicting the active pharmaceutical ingredient (API) content uniformity (CU) for a drug product in which the active is coated onto a core. The model is based on a two-zone mechanistic description of the spray coating process in a perforated coating pan. The relative standard deviation (RSD) of the API CU of the coated tablets was found to be inversely proportional to the square root of the total number of cycles between the spray zone and drying zone that the tablets undergo. The total number of cycles is a function of the number of tablets in the drying zone, the spray zone width, the tablet velocity, the tablet number density, and the total coating time. The sensitivity of the RSD to various critical coating process parameters, such as pan speed, pan load, spray zone width, as well as tablet size and shape was evaluated. Consequently, the critical coating process parameters needed to achieve the desired API CU were determined. Several active film coating experiments at 50, 200, and 400 kg using various pan coaters demonstrated that good correlation between the model predictions and the experimental results for the API CU was achieved.

Experimental and model-based approaches to studying mixing in coating pans.

The focus of this study was the determination of mixing patterns and rates inside a cylindrical coating pan. The research for this study was divided into two parts. The first part examined the mixing pattern and the movement of tablets inside of a coating pan experimentally. The second part consisted of using a DEM (Discrete Element Model) simulation to evaluate mixing in the coating pan in silico. Mixing was investigated as a function of the rate of rotation of the pan and the number of revolutions. Mixing rates were measured in two directions--axial--from the front of the unit to the back of the unit along its axis and radial/angular--in the plane orthogonal to its axis. Radial/angular mixing was faster than axial mixing--the coating pan was found to be well-mixed across the axis within 2-8 revolutions as compared to 16-32 revolutions needed for the pan to be well-mixed along the axis. The DEM simulation used for this study is capable of predicting how fast the tablets mix in the coating pan. It does so by explicitly modeling the motion of individual tablets in the unit. Model predictions were verified by comparing the simulated mixing in the coating pan to the experiments. The simulated mixing process is found to be slightly slower than the experimentally observed mixing, which means that the simulations give a conservative estimate of mixing rates. The model can also be used to calculate the residence time distribution of the tablets in a spray zone of a given area.

Developing and evaluating techniques for localizing pollutant emission sources with open-path Fourier transform infrared measurements and wind data.

This paper presents the simulation and field evaluation results of two approaches to localize pollutant emission sources with open-path Fourier transform infrared (OP-FTIR) spectroscopy. The first approach combined the plume's peak location information reconstructed from the Smooth Basis Function Minimization (SBFM) algorithm and the wind direction data to calculate source projection lines. In the second approach, the plume's peak location was determined with the Monte Carlo methodology by randomly sampling within the beam segment having the largest path-integrated concentration. We first conducted a series of simulation studies to investigate the sensitivity of using different basis functions in the SBFM algorithm. It was found that fitting with the beta and Weibull basis functions generally gave better estimates of the peak locations than with the normal basis function when the plumes were mainly within the OP-FTIR's monitoring line. However, for plumes that were symmetric to the peak position or spread over the OP-FTIR, fitting with the normal basis function gave better performance. In the field experiment, two tracer gases were released simultaneously from two locations and the OP-FTIR collected data downwind from the sources with a maximum beam path length of 97 m. For the first approach, the release locations were within the 0.25- to 0.5-probability area only after the uncertainty of the peak locations was included in the calculation process. The second approach was easy to implement and still performed as satisfactorily as the first approach. The distances from the sources to the best-fit lines (i.e., the regression lines) of the estimated locations were smaller than 10 m.

Evaluation of risk and benefit in the implementation of near-infrared spectroscopy for monitoring of lubricant mixing.

On-line near-infrared spectroscopy (NIRS) was used to monitor lubricant blending to ensure the quality of the final dosage form. A quantitative multivariate NIR model was developed using different lubricant concentration levels. Real-time model predictions correlated well with the expected lubricant concentration during blending, which allowed determination of blend quality. The significance of sensor location on the blender at different fill levels was evaluated. The capability of this application was further assessed by real-time study of blending dynamics under varying process conditions and raw material attributes. The response of the developed NIR method to sudden spikes in analyte concentration, changes in raw material attributes, and perturbations to standard mixing procedures was evaluated. This study allows an understanding of risk factors associated with the implemented technology, and its ability to accurately monitor the process events. Furthermore, it highlights the importance of proper selection of processing conditions and raw material attributes to improve process robustness.