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This study describes how near infrared (NIR) spectroscopy can be used to predict the dissolution of bilayer tablets as a non-destructive approach. Tablets in this study consist of two active pharmaceutical ingredients (APIs) physically separated in layers and manufactured under three levels of hardness. NIR spectra were individually acquired for both layers in diffuse reflectance mode. Reference dissolution profile values were obtained using dissolution apparatus & HPLC. A multivariate partial least squares (PLS) calibration model was developed for each API relating its dissolution profile to spectral data. This calibration model was used to predict dissolution profiles of an independent test set and results of the prediction were compared using model free approaches i.e. dissimilarity (f1) & similarity (f2) factors to assure similarity in dissolution performance.

Process analytical technology in the pharmaceutical industry requires the monitoring of critical quality attributes (CQA) through calibrated models. However, the development, implementation, and maintenance of these quantitative models are both resource and time-intensive. This study proposes the implementation of a non-linear iterative optimization technology (IOT) to study the magnitude of analytical errors when the calibration tablet used to extract the λ vector deviates physically and chemically from the test samples. IOT is based on mathematical optimization of excess spectral absorbance. It requires minimum calibration effort and allows simultaneous prediction of the entire formulation instead of only the active pharmaceutical ingredient (API), with just one standard and pure component spectral data. Unlike Partial Least Squares (PLS), which requires the development of standards to incorporate variations in the process, this non-destructive methodology minimizes significant calibration effort by developing a mathematical model that uses only one standard and spectral information of pure powders present in the tablet. The method described in this study allows a fast re-calculation to include factors such as change of spectroscopic instruments, variations in raw materials, environmental conditions, and methods of tablet preparation. The robustness of the proposed approach for variation in compaction (physical changes) and variation in composition (chemical changes) was evaluated for correlated and uncorrelated formulations. For uncorrelated formulation a PLS model was also constructed to compare the robustness of the proposed methodology. The RMSEP of API in target formulation predicted using non-linear IOT method was varied from 0.17 to 1.50 depends on compaction of tablet chosen to compute λ vector. On the other hand, the RMSEP of API in target formulation predicted using PLS-based model was varied from 0.13 to 0.57 depending on compaction of tablet. The additional accuracy achieved in PLS based model required significant calibration effort of preparing 84 tablets compared to just one in proposed non-linear IOT method.

In-line measurements of low dose blends in the feed frame of a tablet press were performed for API concentration levels as low as 0.10% w/w. The proposed methodology utilizes the advanced sampling capabilities of a Spatially Resolved Near-Infrared (SR-NIR) probe to develop Partial Least-Squares calibration models. The fast acquisition speed of multipoint spectra allowed the evaluation of different numbers of co-adds and feed frame paddle speeds to establish the optimum conditions of data collection to predict low potency blends. The interaction of the feed frame paddles with the SR-NIR probe was captured with high resolution and allowed the implementation of a spectral data selection criterion to remove the effect of the paddles from the calibration and testing process. The method demonstrated accuracy and robustness when predicting drug concentrations across different feed frame paddle speeds.

Capping is the frequently observed mechanical defect in tablets arising from the sub-optimal selection of the formulation composition and their robustness of response toward process parameters. Hence, overcoming capping propensity based on the understanding of suitable process and material parameters is of utmost importance to expedite drug product development. In the present work, 26 diverse formulations were characterized at commercial tableting condition to identify key tablet properties influencing capping propensity, and a predictive model based on threshold properties was established using machine learning and multivariate tools. It was found that both the compaction parameters (i.e., compaction pressure, radial stress transmission characteristics, and Poisson's ratio), and the material properties, (i.e., brittleness, yield strength, particle bonding strength and elastic recovery) strongly dictate the capping propensity of a tablet. In addition, ratio of elastic modulus in the orthogonal direction in a tablet and its variation with porosity were notable quantitative metrics of capping occurrence.

Raman chemical mapping is an inherently slow analysis tool. Accurate and robust multivariate analysis algorithms, which require least amount of time and effort in method development are desirable. Calibration-free regression and resolution approaches such as classical least squares (CLS) and multivariate curve resolution using alternating least squares (MCR-ALS), respectively, help in reducing the resources required for method development. However, conventional CLS does not consider appropriate constraints, which may result in negative and/or greater than 100 % Raman concentration scores, while MCR-ALS may not always be as accurate as regression-based algorithms. Linear iterative optimization technology (IOT) is another calibration-free algorithm, which with appropriate constraints has previously shown promise in online and offline pharmaceutical mixture composition determination. This paper aims to evaluate the performance of the linear IOT algorithm for Raman chemical mapping of the active pharmaceutical ingredient (API), diluent, and lubricant in pharmaceutical tablets. Two pre-processing strategies were applied to the raw Raman mapping spectra. The results were compared with CLS (current reference method) and MCR-ALS. Special emphasis was given to mapping at low Raman exposure times to enable feasible total acquisition times (5 h). The quality of IOT/CLS/MCR-ALS estimated Raman concentration predictions were assessed by calculating a correlation factor between the spectrum corresponding to the maximum predicted concentration (or resolved spectra) of a component for IOT/CLS (or MCR-ALS) and the pure powder component spectrum. The Raman chemical maps were visualized, and the average Raman concentrations scores were compared. The results demonstrated the utility of IOT in Raman chemical mapping of pharmaceutical tablets. The diluent (lactose) and API (semi-fine APAP) used in this study were reliably estimated by IOT at relatively short Raman exposure times. On the other hand, as expected, the lubricant (magnesium stearate) could not be detected in any of the cases investigated here, irrespective of the algorithm used. Overall, for the API and diluent used in this formulation as well as the chemical mapping conditions, linear IOT seemed to better estimate the pure spectrum intensities and the average Raman scores (closer to CLS) in comparison to MCR-ALS. Moreover, application of appropriate constraints in linear IOT avoided the presence of negative and/or greater than 100 % Raman concentration scores, as observed in CLS-based Raman chemical maps.

A multiscale model combines the computational efficiency of a macro-scale model and the accuracy of a micro-scale model. With the current cyberinfrastructure resources available, using more computationally intensive and concurrent multiscale models are more feasible. This study proposes to use Discrete Element Method (DEM) and a Population Balance Model (PBM) in a simultaneous manner to model the granulation process of a pharmaceutical product inside a high shear granulator. The DEM provides the collision data while the PBM helps in predicting the macroscale phenomena like aggregation and breakage. The execution of each of the components is governed by a multilevel job scheduler which allocates resources. This method of using shorter bursts of each simulation led to faster simulation times as well as a more accurate model of the high shear granulator. The Quality by Design (QbD) approach is addressed using such a modelling framework and it also helps us understand the granulation process in a quantitative as well as in a mechanistic manner.

For several systems of interest, inputs to the system may be unknown. Thus, one may be interested in estimating the unknown inputs alongwith the states. In this work, we modify the approach proposed by Madapusi and Bernstein (2007) for estimating the states and the unknown inputs. The approach, applicable to systems with feedthrough, obtains a minimum variance unbiased estimate of the states. The inputs are estimated after the filtered states are obtained and do not require any restrictive assumptions about the dynamic variation of the inputs. Compared to Madapusi and Bernstein (2007), our approach differs in the prediction step. In particular, we use the estimated input in the prediction step while it has not been considered in the work of Madapusi and Bernstein (2007). Our proposed modification reduces the number of constraints that need to be satisfied by the filter gain thereby increasing the applicability of the approach. The efficacy of the approach is demonstrated by applying it to estimate unknown inputs and states in a self powered neutron detector, that is widely used in nuclear reactors to monitor the neutron flux.