Effect of Tapped Density, Compacted Density, and Drug Concentration on Light-Induced Fluorescence Response as a Process Analytical Tool

Shah, Ishan G.; Ely, Kevin J.; Stagner, William C.

doi:10.1002/jps.24386

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…The compression force ranged from 4.9 to 44.1 which was similar to the range used in the previous reported study (13). The predictive equation describing the effect of % RH and CF on the LIF signal was statistically significant with an adjusted R 2 =0.9436 and p<0.0001.…”

Section: Resultssupporting

confidence: 70%

“…Over a defined compression range, an increase in compression force will lead to an increase in material density. As noted earlier, the LIF signal is a function of material density and API concentration (13). Therefore, in this particular study, compression force can be considered a measureable process variable that is a surrogate for material density.…”

Section: Introductionmentioning

confidence: 89%

“…A detailed description of the purpose-built LIF sensor including a fiber optic probe has been previously described (13). A photograph of the portable LIF sensor is shown in Fig.…”

Section: Purpose-built Lif Sensormentioning

confidence: 99%

“…This technique has also shown to be promising in nondestructively measuring online active pharmaceutical ingredient (API) tablet concentration (11,12). Previously, the authors reported the effect of different tapped densities, compacted densities, concentration, and the interaction of material density-concentration on LIF response (13). The fluorescent response to powder mixtures containing 0.25 to 10.00% w/w of the same fluorescent API used in this study was evaluated over a density range of about 0.6 to 1.4 g/ cm 3 .…”

Section: Introductionmentioning

confidence: 99%

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Effect of Percent Relative Humidity, Moisture Content, and Compression Force on Light-Induced Fluorescence (LIF) Response as a Process Analytical Tool

Shah

Stagner

2015

AAPS PharmSciTech

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Abstract. The effect of percent relative humidity (16-84% RH), moisture content (4.2-6.5% w/w MC), and compression force (4.9-44.1 kN CF) on the light-induced fluorescence (LIF) response of 10% w/w active pharmaceutical ingredient (API) compacts is reported. The fluorescent response was evaluated using two separate central composite designs of experiments. The effect of % RH and CF on the LIF signal was highly significant with an adjusted R 2 =0.9436 and p<0.0001. Percent relative humidity (p=0.0022), CF (p<0.0001), and % RH 2 (p=0.0237) were statistically significant factors affecting the LIF response. The effects of MC and CF on LIF response were also statistically significant with a p value <0.0001 and adjusted R 2 value of 0.9874. The LIF response was highly impacted by MC (p<0.0001), CF (p<0.0001), and MC 2 (p=0022). At 10% w/w API, increased % RH, MC, and CF led to a nonlinear decrease in LIF response. The derived quadratic model equations explained more than 94% of the data. Awareness of these effects on LIF response is critical when implementing LIF as a process analytical tool.KEY WORDS: design of experiments (DOE); fluorescence quenching; light-induced fluorescence (LIF); moisture; process analytical technology (PAT).

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Section: Resultssupporting

confidence: 70%

Section: Introductionmentioning

confidence: 89%

“…A detailed description of the purpose-built LIF sensor including a fiber optic probe has been previously described (13). A photograph of the portable LIF sensor is shown in Fig.…”

Section: Purpose-built Lif Sensormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of Percent Relative Humidity, Moisture Content, and Compression Force on Light-Induced Fluorescence (LIF) Response as a Process Analytical Tool

Shah

Stagner

2015

AAPS PharmSciTech

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“…Those regression coefficients whose 95% confidence bars do not cross the origin, are considered statistically significant variables (24). Additionally, the interpretation details of LVM methods can be found in our previous publications (7)(8)(9)28).…”

Section: Principal Component Regressionmentioning

confidence: 99%

Employing Multivariate Statistics and Latent Variable Models to Identify and Quantify Complex Relationships in Typical Compression Studies

et al. 2020

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The effect of storage condition (% RH) on flufenamic acid:nicotinamide (FFA:NIC) cocrystal compressibility, compactibility, and tabletability profiles was not observed after visual evaluation or linear regression analysis. However, multivariate statistical analysis showed that storage condition had a significant effect on each compressional profile. Shapiro and Heckel equations were used to determine the compression parameters: porosity, Shapiro's compression parameter (f), densification factor (Da), plastic yield pressure (YPpl), and elastic yield pressure (YPel). Latent variable models such as exploratory factor analysis, principal component analysis, and principal component regression were employed to decode complex hidden main, interaction, and quadratic effects of % RH and the compression parameters on FFA:NIC tablet mechanical strength (TMS). Statistically significant correlations between f and Da, f and YPpl, and Da and YPel supported the idea that both rearrangement and fragmentation, and plastic deformation are important to FFA:NIC TMS. To the authors knowledge, this is the first time that simultaneously operating dual mechanisms of fragmentation and plastic deformation in low and midrange compression, and midrange plastic deformation have been identified and reported. A quantitative PCR model showed that f, Da, and YPel had statistically significant main effects along with a significant antagonist storage condition-porosity "conditional interaction effect". f exhibited a 2.35 times greater impact on TMS compared to Da. The model root-mean-square error at calibration and prediction stages were 0.04 MPa and 0.08 MPa, respectively. The R 2 values at the calibration stage and at the prediction stage were 0.9005 and 0.7539, respectively. This research demonstrated the need for caution when interpreting the results of bivariate compression data because complex latent interrelationships may be hidden from visual assessment and linear regression analysis, and result in false data interpretation as illustrated in this report.

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Light‐Induced Fluorescence [Process Analytical Method]

Domike

2018

Encyclopedia of Analytical Chemistry

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Light‐induced fluorescence (LIF) as a process analytical method has relevance in many industrial applications where the monitoring of a fluorophore is desired. Subject to the fluorescence characteristics of the materials being monitored, LIF can rapidly provide highly selective and sensitive assessment of the molecule of interest. Typical configuration of a LIF instrument, equipment challenges, and material challenges are described.

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Effect of Tapped Density, Compacted Density, and Drug Concentration on Light-Induced Fluorescence Response as a Process Analytical Tool

Cited by 7 publications

References 21 publications

Effect of Percent Relative Humidity, Moisture Content, and Compression Force on Light-Induced Fluorescence (LIF) Response as a Process Analytical Tool

Effect of Percent Relative Humidity, Moisture Content, and Compression Force on Light-Induced Fluorescence (LIF) Response as a Process Analytical Tool

Employing Multivariate Statistics and Latent Variable Models to Identify and Quantify Complex Relationships in Typical Compression Studies

Light‐Induced Fluorescence [Process Analytical Method]

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