Evaluating the Improvement of Blend Potency Measurements in the Feed Frame of a Rotary Tablet Press Using Combined NIR and Raman Spectroscopy.

This study investigated the added value of combining both near-infrared (NIR) and Raman spectroscopy into a single NIRaman Combi Fiber Probe for in-line blend potency determination in the feed frame of a rotary tablet press. A five-component platform formulation was used, containing acetylsalicylic acid as the Active Pharmaceutical Ingredient (API). Calibration models for the determination of 1 and 5%w/w label claim tablets were developed using NIR and Raman spectra of powder blends ranging from 0.75 to 1.25%w/w and 3.75 to 6.25%w/w API, respectively. Step-change experiments with deliberate 10% deviation steps from the label claims were performed, from which the collected spectra were used for model validation. For model development and validation, low-level data fusion was explored through concatenation of preprocessed NIR and Raman spectra. Mid-level data fusion was also evaluated, based on extracted features of the preprocessed data. Herewith, score vectors were extracted by transforming preprocessed spectra through Principal Component Analysis, followed by critical feature selection through Elastic Net Regression. Partial Least Squares regression was applied to regress singular, low-level or mid-level fused data versus blend potency. It could be concluded that irrespective of the data fusion technique, an increase in Step-Change Sensitivity (SCS) and decrease in Root Mean Squared Error (RMSE) was observed when predicting the 5%w/w step-change experiment. For the prediction of the 1%w/w step-change experiment, no added benefit with regard to SCS and RMSE was observed due to the addition of the noisy NIR spectra.

Exploring dissolved N2O characteristics and unearthing indirect N2O emission factors in the shallow groundwater of paddy and upland fields.

Indirect emissions of nitrous oxide (N2O) stemming from nitrogen (N) leaching in agricultural fields constitute a significant contributor to atmospheric N2O. Groundwater nitrate (NO3--N) pollution is severe in the Ningxia Yellow River Irrigation Area (NYRIA), coupled with high NO3--N leaching, exacerbates the risk of indirect N2O emissions from groundwater. Over two years of field observations, this study investigated the characteristics and interannual variations of dissolved N2O (dN2O) concentrations and indirect N2O emission factors (EF5g) in shallow groundwater. The research focused on three typical farmlands in the NYRIA, each subjected to six levels of N fertilizer application. The mean dN2O concentrations in the groundwater of paddy, corn and vegetable fields were 5.17, 8.40 and 16.35 µg N·L-1, respectively. Notably, the dN2O concentrations in the shallow groundwater of upland fields exceeded those in paddy fields, with maximum levels in vegetable fields nearly an order of magnitude higher. Elevated N application significantly increased dN2O concentrations across various farmlands, showing statistically significant variation. However, differences in EF5g-A and EF5g-B within the same farmland were negligible. Denitrification was the primary process contributing to N2O production in groundwater, with nitrification also played a crucial role in upland fields. Factors such as NO3--N, NH4+-N, dissolved oxygen (DO), and pH critically influenced N2O production. EF5g-B, which considers the NO3--N consumption during denitrification processes in groundwater, was deemed more appropriate than EF5g-A for assessing the indirect N2O emission in the NYRIA. The EF5g of agricultural fields exhibited minimal sensitivity to N input but was significantly affected by other factors, such as the planting pattern. The study revealed the rationality of adopting EF5g-B in assessing indirect N2O emissions, providing valuable insights for N management strategies in regions with high NO3--N leaching. Minimizing N fertilizer application while ensuring crop yield, especially in upland fields, is beneficial for reducing N2O emissions.

CTPredictor: A comprehensive and robust framework for predicting cell types by integrating multi-scale features from single-cell Hi-C data.

Single-cell Hi-C (scHi-C) has emerged as a powerful technology for deciphering cell-to-cell variability in three-dimensional (3D) chromatin organization, providing insights into genome-wide chromatin interactions and their correlation with cellular functions. Nevertheless, the accurate identification of cell types across different datasets remains a formidable challenge, hindering comprehensive investigations into genome structure. In response, we introduce CTPredictor, an innovative computational method that integrates multi-scale features to accurately predict cell types in various datasets. CTPredictor strategically incorporates three distinct feature sets, namely, small intra-domain contact probability (SICP), smoothed small intra-domain contact probability (SSICP), and smoothed bin contact probability (SBCP). The resulting fusion classification model significantly enhances the accuracy of cell type prediction based on single-cell Hi-C data (scHi-C). Rigorous benchmarking against established methods and three conventional machine learning approaches demonstrates the robust performance of CTPredictor, positioning it as an advanced tool for cell type prediction within scHi-C data. Beyond its prediction capabilities, CTPredictor holds promise in illuminating 3D genome structures and their functional significance across a wide array of biological processes.

Ditch level-dependent N removal capacity of denitrification and anammox in the drainage system of the Ningxia Yellow River irrigation district.

Drainage networks, consisting of different levels of ditches, play a positive role in removing reactive nitrogen (N) via self-purification before drainage water returns to natural water bodies. However, relatively little is known about the N removal capacity of irrigation agricultural systems with different drainage ditch levels. In this study, we employed soil core incubation and soil slurry 15N paired tracer techniques to investigate the N removal rate (i.e., N2 flux), denitrification, anaerobic ammonium oxidation (anammox), and dissimilatory nitrate reduction to ammonium (DNRA) rates in the Ningxia Yellow River irrigation district at various ditch levels, including field ditches (FD), paddy field ditches (PFD), lateral ditches (LD1 and LD2), branch ditches (BD1, BD2, BD3), and trunk ditches (TD). The results indicated that the N removal rate ranged from 44.7 to 165.22 nmol N g-1 h-1 in the ditches, in the following decreasing order: trunk ditches > branch ditches > paddy field ditches > lateral ditches > field ditches. This result suggested that the N removal rate in drainage ditches is determined by the ditch level. In addition, denitrification and anammox were the primary pathways for N removal in the ditches, contributing 68.40-76.64 % and 21.55-30.29 %, respectively, to the total N removal. In contrast, DNRA contributed only 0.82-2.15 % to the total nitrate reduction. The N removal rates were negatively correlated with soil EC and pH and were also constrained by the abundances of denitrification functional genes. Overall, our findings suggest that the ditch level should be considered when evaluating the N removal capacity of agricultural ditch systems.

Drainage ditches are significant sources of indirect N2O emissions regulated by available carbon to nitrogen substrates in salt-affected farmlands.

Agriculture is a main source of nitrous oxide (N2O) emissions. In agricultural systems, direct N2O emissions from nitrogen (N) addition to soils have been widely investigated, whereas indirect emissions from aquatic ecosystems such as ditches are poorly known, with insufficient data available to refine the IPCC emission factor. In this contribution, in situ N2O emissions from two ditch water‒air interfaces based on a diffusion model were investigated (almost once per month) from June 2021 to December 2022 in an intensive arable catchment with high N inputs and salt-affected conditions in the Qingtongxia Irrigation District, northwestern China. Our results implied that agricultural ditches (mean 148 µg N m-2 h-1) were significant sources for N2O emissions, and were approximately 2.1 times greater than those of the Yellow River directly connected to ditches. Agronomic management strategies increased N2O fluxes in summer, while precipitation events decreased N2O fluxes. Agronomic management strategies, including fertilization (294--540 kg N hm-2) and irrigation on farmland, resulted in enhanced diffuse N loads in drain water, whereas precipitation diluted the dissolved N2O concentration in ditches and accelerated the ditch flow rate, leading to changes in the residence time of N-containing substances in water. The spatial analysis showed that N2O fluxes (202-233 µg N m-2 h-1) in the headstream and upstream regions of ditches due to livestock and aquaculture pollution sources were relatively high compared to those in the midstream and downstream regions (100-114 µg N m-2 h-1). Furthermore, high available carbon (C) relative to N reduced N2O fluxes at low DOC:DIN ratio levels by inhibiting nitrification. Spatiotemporal variations in the N2O emission factor (EF5) across ditches with higher N resulted in lower EF5 and a large coefficient of variation (CV) range. EF5 was 0.0011 for the ditches in this region, while the EF5 (0.0025) currently adopted by the IPCC is relatively high. The EF5 variation was strongly controlled by the DOC:DIN ratio, TN, and NO3--N, while salinity was also a nonnegligible factor regulating the EF5 variation. The regression model incorporating NO3--N and the DOC:DIN ratio could greatly enhance the predictions of EF5 for agricultural ditches. Our study filled a key knowledge gap regarding EF5 from agricultural ditches in salt-affected farmland and offered a field investigation for refining the EF5 currently used by the IPCC.

Phase-appropriate Application of Process Analytical Technology for Early Pharmaceutical Development of Oral Solid Dosage Forms-the Case Study of Uniformity Screening of Dosage Units and Blends.

Process analytical technology (PAT) in late-stage drug product development is typically used for real-time process monitoring, in-process control, and real-time release testing. In early research and development (R&D), PAT usage is limited as the manufacturing scale is relatively small with frequent changes and only a few batches are produced on an annual basis. However, process understanding is critical at early R&D in order to identify process and formulation boundaries, so PAT applications could be particularly useful in early-stage R&D. For oral solid dosage form, conventional HPLC-based content uniformity (CU) methods with sampling of 3 tablets per stratified sampling location in early R&D are typically not sufficient to identify these manufacturing process boundaries and temporal profile. Here, we report a screening CU method based on a multivariate model using transmission Raman spectroscopy (TRS) data on a phase-appropriate calibration set of only 16 tablets. This initial model was used for multiple pre-GMP development batches to provide critical information about blend uniformity and content uniformity (CU). In this work, the precision of the TRS method was evaluated; multiple spectral preprocessing approaches were compared regarding their effects on measurement precision as well as their ability to mitigate the photo bleaching effects during precision experiments. Overall, the TRS-based CU method was much faster than a traditional HPLC-based method allowing a much larger number of tablets to be screened. This larger number of analyzed tablets enabled the processes boundaries and temporal changes in CU to be identified while providing proper statistical assurance on product quality.

Salinity and high pH reduce denitrification rates by inhibiting denitrifying gene abundance in a saline-alkali soil.

Denitrification, as the main nitrogen (N) removal process in farmland drainage ditches in coastal areas, is significantly affected by saline-alkali conditions. To elucidate the effects of saline-alkali conditions on denitrification, incubation experiments with five salt and salt-alkali gradients and three nitrogen addition levels were conducted in a saline-alkali soil followed by determination of denitrification rates and the associated functional genes (i.e., nirK/nirS and nosZ Clade I) via N2/Ar technique in combination with qPCR. The results showed that denitrification rates were significantly decreased by 23.83-50.08%, 20.64-57.31% and 6.12-54.61% with salt gradient increasing from 1 to 3‰, 8‰, and 15‰ under 0.05‰, 0.10‰ and 0.15‰ urea addition conditions, respectively. Similarly, denitrification rates were significantly decreased by 44.57-63.24% with an increase of the salt-alkali gradient from 0.5 to 8‰. The abundance of nosZ decreased sharply in the saline condition, while a high salt level significantly decreased the abundance of nirK and nirS. In addition, the increase of nitrogen concentration attenuated the reduction of nirK, nirS and nosZ gene abundance. Partial least squares regression (PLSR) models demonstrated that salinity, dissolved oxygen (DO) in the overlying water, N concentration, and denitrifying gene abundance were key determinants of the denitrification rate in the saline environment, while pH was an additional determinant in the saline-alkali environment. Taken together, our results suggest that salinity and high pH levels decreased the denitrification rates by significantly inhibiting the abundance of the denitrifying genes nirK, nirS, and nosZ, whereas increasing nitrogen concentration could alleviate this effect. Our study provides helpful information on better understanding of reactive N removal and fertilizer application in the coastal areas.

Development of a Near-Infrared Spectroscopy (NIRS)-Based Characterization Approach for Inherent Powder Blend Heterogeneity in Direct Compression Formulations.

With the advent of continuous direct compression (CDC) process, it becomes increasingly desirable to characterize inherent powder blend heterogeneity at a small batch scale for a robust and CDC-amenable formulation. To accomplish this goal, a near infrared spectroscopy (NIRS)-based characterization approach was developed and implemented on multiple direct compression (DC) blends in this study, with the intended purpose of complementing existing formulation development tools and enabling to build an early CMC data package for late-phased process analytical technology (PAT) method development. Three fumaric acid DC blends, designed to harbor varied degrees of inherent blend heterogeneity, were employed. Near infrared spectral data were collected on a kg-scale batch blender via both time- and angle-based triggering modes. The time-triggered data were used to investigate the blending heterogeneity with respect to rotation angles, while the angle-triggered data were used to provide blending variability characterization and compare against off-line HPLC-based results. The time-triggered data revealed that the greatest blend variability was observed between revolutions, while the blending variability within a single revolution stayed relatively low with respect to rotation angles. This confirmed earlier literature findings that the bottom layer of powder blends tends to move with the blender within each revolution, and the most intense powder mixing takes place across revolutions. This also indicates the use of blending speed and the number of co-adds are not able to increase sampling volume to improve signal-to-noise ratio under a tumble-bin blender as what were typically done in a feedframe application. The angle-triggered data showed that there is a consistent trend between NIRS and HPLC-based methods on characterizing blend heterogeneity across the blends at a given sample size. This study contributes to establishing NIRS as a potential characterization approach for inherent powder blend heterogeneity for early R&D. It also highlights the promise of continuous characterization of inherent powder blend heterogeneity from gram scale to mini-batch CDC scale.

Spectroscopy-Based Local Modeling Method for High-Throughput Quantification of Nucleic Acid Loading in Lipid Nanoparticles.

Lipid nanoparticles (LNPs) are the most widely investigated delivery systems for nucleic acid-based therapeutics and vaccines. Loading efficiency of nucleic acids may vary with formulation conditions, and it is considered one of the critical quality attributes of LNP products. Current analytical methods for quantification of cargo loading in LNPs often require external standard preparations and preseparation of unloaded nucleic acids from LNPs; therefore, they are subject to tedious and lengthy procedures, LNP stability, and unpredictable recovery rates of the separated analytes. Here, we developed a modeling approach, which was based on locally weighted regression (LWR) of ultraviolet (UV) spectra of unpurified samples, to quantify the loading of nucleic acid cargos in LNPs in-situ. We trained the model to automatically tune the training library space according to the spectral features of a query sample so as to robustly predict the nucleic acid cargo concentration and rank loading capacity with similar performance as the more complicated experimental approaches. Furthermore, we successfully applied the model to a wide range of nucleic acid cargo species, including antisense oligonucleotides, single-guided RNA, and messenger RNA, in varied lipid matrices. The LWR modeling approach significantly saved analytical time and efforts by facile UV scans of 96-well sample plates within a few minutes and with minimal sample preprocessing. Our proof-of-concept study presented the very first data mining and modeling strategy to quantify nucleic acid loading in LNPs and is expected to better serve high-throughput screening workflows, thereby facilitates early-stage optimization and development of LNP formulations.

Do biochar and polyacrylamide have synergistic effect on net denitrification and ammonia volatilization in saline soils?

Salt-affected soils have poor structure and physicochemical properties, which affect soil nitrogen cycling process closely related to the environment, such as denitrification and ammonia volatilization. Biochar and polyacrylamide (PAM) have been widely used as soil amendments to improve soil physicochemical properties. However, how they affect denitrification and ammonia volatilization in saline soils is unclear. In this study, the denitrification and ammonia volatilization rates were measured in a saline soil field ameliorated with three biochar application rates (0%, 2%, and 5%, w/w) and three PAM application rates (0‰, 0.4‰, and 1‰, w/w) over 3 years. The results showed that denitrification rates decreased by 23.63-39.60% with biochar application, whereas ammonia volatilization rates increased by 9.82-25.58%. The denitrification and ammonia volatilization rates decreased by 9.87-29.08% and 11.39-19.42%, respectively, following PAM addition. However, there was no significant synergistic effect of biochar and PAM amendments on the denitrification and ammonia volatilization rates. The addition of biochar mainly reduced the denitrification rate by regulating the dissolved oxygen and electrical conductivity of overlying water and absorbing soil nitrate nitrogen. Meanwhile, biochar application increased pH and stimulated the transfer of NH4+-N from soil to overlying water, thus increasing NH3 volatilization rates. Hence, there was a tradeoff between denitrification and NH3 volatilization in the saline soils induced by biochar application. PAM reduced the denitrification rate by increasing the infiltration inorganic nitrogen and slowing the conversion of ammonium to nitrate. Moreover, PAM reduced the concentration of NH4+-N in the overlying water through absorbing soil ammonium and inhibiting urea hydrolysis, thereby decreasing NH3 volatilization rate.

Multivariate Spectroscopic Method Lifecycle Management as Part of the Quality Management System.

Multivariate model based spectroscopic methods require model maintenance through their lifecycle. A survey conducted by the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) in 2019 showed that regulatory reporting categories for the model related changes can be a hurdle for the routine use of these types of methods. This article introduces industry best practices on multivariate method and model lifecycle management within the Pharmaceutical Quality System. Case studies are provided to demonstrate how the Established Conditions and Post-Approval Change Management Protocol concepts may be leveraged to allow regulatory flexibility for change management and to encourage the use of these techniques for the development and commercialization of pharmaceutical products.

Sample Mass Estimate for the Use of Near-Infrared and Raman Spectroscopy to Monitor Content Uniformity in a Tablet Press Feed Frame of a Drug Product Continuous Manufacturing Process.

Recently, feed frame-based process analytical technology measurements used to assure product quality during continuous manufacturing processes have received significant attention. These measurements are able to accurately determine uniformity of the powder blend before compression, and in these applications, it is necessary to understand the interrogated sample volume per measurement. This understanding ensures that the blend measurement can be indicative of the uniformity of the final dosage form. A scientifically sound approach is proposed here to estimate sample mass for a continuous manufacturing process that utilizes either near infrared or Raman spectroscopy. A wide range of commercially available probes with varying spot diameters are considered. By comparing near infrared and Raman spectroscopy, an optimal range of probe spot diameters was identified in order to reach an estimated sample mass between 50 and 500 mg for pharmaceutical blends per measurement, which is equivalent to common tablet weight ranges for solid oral dosage forms currently on the market.

Fractal and Polarization Properties of Light Scattering Using Microcrystalline Pharmaceutical Aggregates.

Fractal and polarization analysis of diffusively scattered light is applied to determine the complex relationship between fractal dimension of structural morphology and concentration of chemically active ingredients in two pharmaceutical mixture systems including a series of binary mixtures of acetaminophen in lactose and three multicomponent blends with a proprietary active ingredient. A robust approach is proposed to identify and filter out multiple- and single-scattering components of scattering indicatrix. The fractal dimension extracted from scattering field reveals complex structural details of the sample, showing strong dependence on low-dose drug concentration in the blend. Low-angle diffraction shows optical "halo" patterns near the angle of specular reflection caused by light refraction in microcrystalline aggregates. Angular measurements of diffuse reflection demonstrate noticeable dependence of Brewster's angle on drug concentration. It is shown that the acetaminophen microcrystals produce scattered light depolarization due to their optical birefringence. The light scattering measurement protocol developed for diffusively scattered light by microcrystalline pharmaceutical compositions provides a novel approach for the pattern recognition, analysis and classification of materials with a low concentration of active chemical ingredients.

Niclosamide and its derivative DK-520 inhibit RANKL-induced osteoclastogenesis.

Niclosamide is a potent inhibitor of osteoclastogenesis and bone remodeling. DK-520 is an acyl derivative of Niclosamide and significantly increased both the plasma concentration and the duration of exposure of Niclosamide when dosed orally. However, at present the effect of DK-520 on osteoclastogenesis has not been reported. Here, we investigated whether DK-520 can regulate receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclastogenesis of bone marrow macrophages (BMMs) in vitro. Following induction of BMMs with RANKL for three days, we detected differentiated osteoclasts with typical morphology and high levels of tartrate-resistant acid phosphatase (TRAP), RANKL, and cathepsin K (CTSK) expression. Treatment with either Niclosamide or DK-520 did not affect the viability of osteoclast precursors (OCPs), but significantly inhibited RANKL-induced transdifferentiation of macrophages into OCPs, particularly in the early stage of osteoclastogenesis. Both Niclosamide and DK-520 significantly decreased the relative levels of transcription factor PU.1 mRNA transcripts and dendritic cell-specific transmembrane protein (DC-STAMP), but not v-ATPasev0 d2 protein expression in OCPs. In addition, the inhibitory effect of DK-520 on osteoclastogenesis is realized through impairment of the NF-kB (nuclear factor-κB) and MAPK (mitogen-activated protein kinase) signaling pathways. These results demonstrate that DK-520, like Niclosamide, effectively inhibits the early stage of osteoclastogenesis. The findings presented here, together with its increased oral plasma concentrations and bioavailability, suggest that DK-520 may be a promising drug candidate for treatment of osteoclast-related diseases.

Review of real-time release testing of pharmaceutical tablets: State-of-the art, challenges and future perspective.

In the last decade significant advances have been made in process analytical technologies and digital manufacturing of pharmaceutical oral solid dosage forms leading to enhanced product knowledge and process understanding. These developments provide an excellent platform for realising real-time release testing (RTRT) to eliminate all, or certain, off-line end product tests assuring that the drug product is of intended quality. This review article presents the state of the art, an RTRT development workflow as well as challenges and opportunities of RTRT in batch and continuous manufacturing of pharmaceutical tablets. Critical quality attributes, regulatory aspects and the scientific basis of enabling technologies and models for RTRT are discussed and a systematic development workflow for the robust design of an RTRT environment is presented. This includes the discussion of key considerations for the identification of the critical quality attributes and points of testing as well as the development of the sampling strategy, a hard and/or soft sensor approach and operational procedures. The final sections present two RTRT use cases in an industrial setting as well as critically discuss challenges and provide a future perspective of RTRT.

Specific RANK Cytoplasmic Motifs Drive Osteoclastogenesis.

Upon receptor activator of NF-κB ligand (RANKL) binding, RANK promotes osteoclast formation through the recruitment of tumor necrosis factor (TNF) receptor-associated factors (TRAFs). In vitro assays identified two RANK intracellular motifs that bind TRAFs: PVQEET560-565 (Motif 2) and PVQEQG604-609 (Motif 3), which potently mediate osteoclast formation in vitro. To validate the in vitro findings, we have generated knock-in (KI) mice harboring inactivating mutations in RANK Motifs 2 and 3. Homozygous KI (RANKKI/KI ) mice are born at the predicted Mendelian frequency and normal in tooth eruption. However, RANKKI/KI mice exhibit significantly more trabecular bone mass than age- and sex-matched heterozygous KI (RANK+/KI ) and wild-type (RANK+/+ ) counterparts. Bone marrow macrophages (BMMs) from RANKKI/KI mice do not form osteoclasts when they are stimulated with macrophage colony-stimulating factor (M-CSF) and RANKL in vitro. RANKL is able to activate the NF-κB, ERK, p38, and JNK pathways in RANKKI/KI BMMs, but it cannot stimulate c-Fos or NFATc1 in the RANKKI/KI cells. Previously, we showed that RANK signaling plays an important role in Porphyromonas gingivalis (Pg)-mediated osteoclast formation by committing BMMs into the osteoclast lineage. Here, we show that RANKL-primed RANKKI/KI BMMs are unable to differentiate into osteoclasts in response to Pg stimulation, indicating that the two RANK motifs are required for Pg-induced osteoclastogenesis. Mechanistically, RANK Motifs 2 and 3 facilitate Pg-induced osteoclastogenesis by stimulating c-Fos and NFATc1 expression during the RANKL pretreatment phase as well as rendering c-Fos and NFATc1 genes responsive to subsequent Pg stimulation. Cell-penetrating peptides (CPPs) conjugated with RANK segments containing Motif 2 or 3 block RANKL- and Pg-mediated osteoclastogenesis. The CPP conjugates abrogate RANKL-stimulated c-Fos and NFATc1 expression but do not affect RANKL-induced activation of NF-κB, ERK, p38, JNK, or Akt signaling pathway. Taken together, our current findings demonstrate that RANK Motifs 2 and 3 play pivotal roles in osteoclast formation in vivo and mediate Pg-induced osteoclastogenesis in vitro.

Characterization of Near-Infrared and Raman Spectroscopy for In-Line Monitoring of a Low-Drug Load Formulation in a Continuous Manufacturing Process.

Reflectance spectroscopy is an excellent candidate for process analytical technology (PAT) applications in continuous manufacturing of pharmaceutical tablets. Spectroscopic methods provide a real-time, nondestructive measurement of the active pharmaceutical ingredient (API) concentration in order to ensure product quality and uniformity. Of particular challenge is the powder blends with low drug loads (<5% w/w) where the measurement of the signal-to-noise and, in turn, precision limit the ability of the method. We evaluate both near-infrared (NIR) and Raman spectroscopy for use in PAT applications by measuring pharmaceutical blends of varying active ingredient concentrations. Both spectrometers are equipped with a fiber-optically coupled probe head for noncontact measurement of powder blends. A mockup of the interface between the spectrometer and powders within the feed frame of a rotary tablet press is used to simulate the movement of powder blends from the mixer to the press. A port on the feed frame allows measurement by NIR or Raman spectroscopy of the blends just before tablet compression. For our model compound, Raman spectroscopy is shown to have a lower limit-of-detection and less day-to-day variability than NIR spectroscopy. Raman spectroscopy was chosen as the PAT platform to support process development, and working distance and spot size were both optimized for use in the feed-frame of a tablet press. Sufficient limit-of-detection was achieved for monitoring active pharmaceutical ingredient concentrations (API) down to 1% w/w during a semicontinuous manufacturing of tablets. An innovative optimization-based model (EIOT) was used to trend API concentration and demonstrated that the process could be capable of detecting out-of-trend material.

First-Principles and Empirical Approaches to Predicting In Vitro Dissolution for Pharmaceutical Formulation and Process Development and for Product Release Testing.

This manuscript represents the perspective of the Dissolution Working Group of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) and of two focus groups of the American Association of Pharmaceutical Scientists (AAPS): Process Analytical Technology (PAT) and In Vitro Release and Dissolution Testing (IVRDT). The intent of this manuscript is to show recent progress in the field of in vitro predictive dissolution modeling and to provide recommended general approaches to developing in vitro predictive dissolution models for both early- and late-stage formulation/process development and batch release. Different modeling approaches should be used at different stages of drug development based on product and process understanding available at those stages. Two industry case studies of current approaches used for modeling tablet dissolution are presented. These include examples of predictive model use for product development within the space explored during formulation and process optimization, as well as of dissolution models as surrogate tests in a regulatory filing. A review of an industry example of developing a dissolution model for real-time release testing (RTRt) and of academic case studies of enabling dissolution RTRt by near-infrared spectroscopy (NIRS) is also provided. These demonstrate multiple approaches for developing data-rich empirical models in the context of science- and risk-based process development to predict in vitro dissolution. Recommendations of modeling best practices are made, focused primarily on immediate-release (IR) oral delivery products for new drug applications. A general roadmap is presented for implementation of dissolution modeling for enhanced product understanding, robust control strategy, batch release testing, and flexibility toward post-approval changes.

Prediction of Dissolution Profiles From Process Parameters, Formulation, and Spectroscopic Measurements.

This study utilized multiple modeling approaches to predict immediate release tablet dissolution profiles of 2 model drugs: theophylline and carbamazepine. Two sets of designs of experiments were applied based on individual drug characteristics to build in adequate dissolution variability. The tablets were scanned using a near-infrared (NIR) spectrometer and then subjected to in vitro dissolution test at critical time points. Because of the inherent difference in dissolution profiles, a hierarchical modeling approach was applied for theophylline data, whereas global models were constructed from carbamazepine data. The partial least squares models were trained using 3 predictor sets including (1) formulation, material, and process variables, (2) NIR spectra, and (3) a combination of both. The dependent variables of the models were the dissolution profiles, which were presented either as parameters of Weibull fitting curves or raw data. The comparison among the predictive models revealed that the incorporation of NIR spectral information in calibration reduced prediction error in the carbamazepine case but undermined the performance of theophylline models. It suggests that the modeling strategy for dissolution prediction of pharmaceutical tablets should not be universal but on a case-by-case basis.

Characterizing drug product continuous manufacturing residence time distributions of major/minor excipient step changes using near infrared spectroscopy and process parameters.

The material residence time distribution in a continuous manufacturing process can be utilized to develop, design and justify the process control strategy. This paper successfully demonstrates using both major and minor formulation component step changes to determine the system response using either Near Infrared Spectroscopy or process parameters. These options provide development flexibility to determine the system's material residence time earlier in the development process and more cost effectively.