T2 Relaxation Study to Evaluate the Crystalline State of Indomethacin Containing Solid Dispersions Using Time-Domain NMR.

The aim of this study was to demonstrate the usefulness of T2 measurements conducted with a time-domain NMR (TD-NMR) for the characterization of active pharmaceutical ingredients (APIs) containing solid dosage forms. A solid dispersion (SD) and a physical mixture (PM) consisting of indomethacin (IMC) and polyvinylpyrrolidone (PVP) were prepared at different weight ratios as test samples, and then their T2 relaxation curves were measured by TD-NMR. The T2 relaxation curve of IMC was quite different from that of PVP by nature. T2 values of the SD and PM samples became gradually shortened with increasing IMC content. No difference in T2 relaxation curves was observed between SD and PM. By analyzing the T2 relaxation curves in detail, we succeeded in precisely quantifying the IMC contents incorporated in the samples. Next, this study evaluated the T2 relaxation curves of amorphous and crystalline states of powdered IMC. T2 relaxation rate of crystalline IMC was slightly but significantly higher than that of amorphous IMC, proving that the T2 measurement was sensitive enough to detect these differences. Finally, a thermal stress was imposed on SD and PM samples at 60°C for 7 d, and then an amorphous-to-crystalline transformation occurred in IMC in the PM sample and was successfully monitored by T2 measurement. We believe that T2 measurement by TD-NMR is a promising analysis for the characterization of APIs in solid dosage forms, including SD-based pharmaceuticals.

A Novel Approach to Evaluate Amorphous-to-Crystalline Transformation of Active Pharmaceutical Ingredients in Solid Dispersion Using Time-Domain NMR.

The aim of this study was to demonstrate the usefulness of the time-domain NMR (TD-NMR) method to characterize the crystalline state of active pharmaceutical ingredients (APIs) containing a solid dispersion. In this study, indomethacin (IMC) was used as a model for poorly water-soluble API. Solid dispersions of IMC were prepared with polyvinylpyrrolidone (PVP) at different weight ratios. First, we measured the T1 relaxation behavior of solid dispersions. From the result, the T1 relaxation time (T1) changed according to the API content; the T1 tended to increase with increasing API content because the T1 value of amorphous IMC was longer than that of PVP. Next, we tried to monitor the amorphous-to-crystalline transformation of IMC in the solid dispersion during the thermal stress test. In the case of solid dispersion containing 90% IMC, a clear prolongation of the T1 could be observed during the thermal stress test. From the powder X-ray diffraction patterns, the change in T1 relaxation behavior must be caused by the IMC transformation from amorphous to crystalline. From these findings, we were successful in monitoring the IMC amorphous-to-crystalline transformation by the changes in T1 relaxation behavior. Our findings led us to conclude that TD-NMR is a novel approach for the evaluation of crystalline state of APIs in solid dispersions.

A Time-Domain NMR Study of the State of Water in Wet Granules with Different Fillers and Its Contribution to the Wet Granulation Process and to the Characteristics of Granules.

The different states of water incorporated in wet granules were studied by a low-field benchtop 1H-NMR time-domain NMR (TD-NMR) instrument. Wet granules consisting different fillers [cornstarch (CS), microcrystalline cellulose (MCC), and D-mannitol (MAN)] with different water contents were prepared using a high-speed granulator, and then their spin-spin relaxation time (T2) was measured using the NMR relaxation technique. The experimental T2 relaxation curves were analyzed by the two-component curve fitting, and then the individual T2 relaxation behaviors of solid and water in wet granules were identified. According to the observed T2 values, it was confirmed that the molecular mobility of water in CS and MCC granules was more restricted than that in the MAN granule. The state of water appeared to be associated with the drying efficiency and moisture absorption capacity of wet granules. Thus, it was confirmed that the state of water significantly affected the wet granulation process and the characteristics of the resultant granules. In the final phase of this study, the effects of binders on the molecular mobility of water in granulation fluids and wet granules were examined. The state of water in granulation fluids was substantially changed by changing the binders. The difference was still detected in wet granules prepared by addition of these fluids to the fillers. In conclusion, TD-NMR can offer valuable knowledge on wet granulation from the viewpoint of molecular mobility of water.

Modeling of quantitative relationships between physicochemical properties of active pharmaceutical ingredients and tensile strength of tablets using a boosted tree.

OBJECTIVES: The aim of this study was to explore the potential of boosted tree (BT) to develop a correlation model between active pharmaceutical ingredient (API) characteristics and a tensile strength (TS) of tablets as critical quality attributes. METHODS: First, we evaluated 81 kinds of API characteristics, such as particle size distribution, bulk density, tapped density, Hausner ratio, moisture content, elastic recovery, molecular weight, and partition coefficient. Next, we prepared tablets containing 50% API, 49% microcrystalline cellulose, and 1% magnesium stearate using direct compression at 6, 8, and 10 kN, and measured TS. Then, we applied BT to our dataset to develop a correlation model. Finally, the constructed BT model was validated using k-fold cross-validation. RESULTS: Results showed that the BT model achieved high-performance statistics, whereas multiple regression analysis resulted in poor estimations. Sensitivity analysis of the BT model revealed that diameter of powder particles at the 10th percentile of the cumulative percentage size distribution was the most crucial factor for TS. In addition, the influences of moisture content, partition coefficients, and modal diameter were appreciably meaningful factors. CONCLUSIONS: This study demonstrates that BT model could provide comprehensive understanding of the latent structure underlying APIs and TS of tablets.

1H NMR Relaxation Study to Evaluate the Crystalline State of Active Pharmaceutical Ingredients Containing Solid Dosage Forms Using Time Domain NMR.

The aim of this study was to demonstrate the usefulness of the time domain nuclear magnetic resonance (TD-NMR) method to characterize the crystalline state of active pharmaceutical ingredients (APIs) containing solid dosage forms. In this study, carbamazepine and indomethacin are used as models for poorly water-soluble APIs. First, we measured the T1 and T2 relaxation behavior of crystalline and amorphous APIs. From the results, we were able to confirm that the T1 relaxation time measured by TD-NMR is an effective parameter for distinguishing between crystalline and amorphous states in powdered APIs. We then examined physical mixtures of APIs with polyvinylpyrrolidone and their solid dispersion. The results indicated that TD-NMR allows the evaluation of not only the crystalline form of APIs but also the miscibility of APIs and polymers. In the final phase of the study, we conducted continuous monitoring of the crystalline state of APIs incorporated into physical mixtures during the thermal stress test. Conversion to crystalline forms of the APIs was successfully monitored based on the T1 relaxation behavior. Our findings led us to conclude that TD-NMR is useful as a new approach to evaluate the crystalline state of APIs.

In silico predictions of tablet density using a quantitative structure-property relationship model.

The purpose of this study was to explore the potential of a quantitative structure-property relationship (QSPR) model to predict tablet density. First, we calculated 3381 molecular descriptors for 81 active pharmaceutical ingredients (API). Second, we obtained data that were merged with a dataset including powder properties that we had constructed previously. Next, we prepared 81 types of tablet, each containing API, microcrystalline cellulose, and magnesium stearate using direct compression at 120, 160, and 200 MPa, and measured the tablet density. Finally, we applied the boosted-tree machine learning approach to construct a QSPR model and to identify crucial factors from the large complex dataset. The QSPR model achieved statistically good performance. A sensitivity analysis of the QSPR model revealed that molecular descriptors related to the average molecular weight and electronegativity of the API were crucial factors in tablet density, whereas the effects of powder properties were relatively insignificant. Moreover, we found that these descriptors had a positive linear relationship with tablet density. This study indicates that a QSPR approach is possibly useful for in silico prediction of tablet density for tablets prepared using more than a threshold compression pressure, and to allow a deeper understanding of tablet density.

Relationships between response surfaces for tablet characteristics of placebo and API-containing tablets manufactured by direct compression method.

In this study, we evaluated the correlation between the response surfaces for the tablet characteristics of placebo and active pharmaceutical ingredient (API)-containing tablets. The quantities of lactose, cornstarch, and microcrystalline cellulose were chosen as the formulation factors. Ten tablet formulations were prepared. The tensile strength (TS) and disintegration time (DT) of tablets were measured as tablet characteristics. The response surfaces for TS and DT were estimated using a nonlinear response surface method incorporating multivariate spline interpolation, and were then compared with those of placebo tablets. A correlation was clearly observed for TS and DT of all APIs, although the value of the response surfaces for TS and DT was highly dependent on the type of API used. Based on this knowledge, the response surfaces for TS and DT of API-containing tablets were predicted from only two and four formulations using regression expression and placebo tablet data, respectively. The results from the evaluation of prediction accuracy showed that this method accurately predicted TS and DT, suggesting that it could construct a reliable response surface for TS and DT with a small number of samples. This technique assists in the effective estimation of the relationships between design variables and pharmaceutical responses during pharmaceutical development.