Effects of Polymers on Cocrystal Growth in a Drug-Drug Coamorphous System: Relations between Glass-to-Crystal Growth and Surface-Enhanced Crystal Growth.

Coamorphous and cocrystal drug delivery systems provide attractive crystal engineering strategies for improving the solubilities, dissolution rates, and oral bioavailabilities of poorly water-soluble drugs. Polymeric additives have often been used to inhibit the unwanted crystallization of amorphous drugs. However, the transformation of a coamorphous phase to a cocrystal phase in the presence of polymers has not been fully elucidated. Herein, we investigated the effects of low concentrations of the polymeric excipients poly(ethylene oxide) (PEO) and poly(vinylpyrrolidone) (PVP) on the growth of carbamazepine-celecoxib (CBZ-CEL) cocrystals from the corresponding coamorphous phase. PEO accelerated the growth rate of the cocrystals by increasing the molecular mobility of the coamorphous system, while PVP had the opposite effect. The coamorphous CBZ-CEL system exhibited two anomalously fast crystal growth modes: glass-to-crystal (GC) growth in the bulk and accelerated crystal growth at the free surface. These two fast growth modes both disappeared after doping with PEO (1-3% w/w) but were retained in the presence of PVP, indicating a potential correlation between the two fast crystal growth modes. We propose that the different effects of PEO and PVP on the crystal growth modes arose from weaker effects of the polymers on cocrystallization at the surface than in the bulk. This work provides a deep understanding of the mechanisms by which polymers influence the cocrystallization kinetics of a multicomponent amorphous phase and highlights the importance of polymer selection in stabilizing coamorphous systems or preparing cocrystals via solid-based methods.

Predicting Colloidal Stability of High-Concentration Monoclonal Antibody Formulations in Common Pharmaceutical Buffers Using Improved Polyethylene Glycol Induced Protein Precipitation Assay.

Colloidal stability is an important consideration when developing high concentration mAb formulations. PEG-induced protein precipitation is a commonly used assay to assess the colloidal stability of protein solutions. However, the practical usefulness and the current theoretical model for this assay have yet to be verified over a large formulation space across multiple mAbs and mAb-based modalities. In the present study, we used PEG-induced protein precipitation assays to evaluate colloidal stability of 3 mAbs in 24 common formulation buffers at 20 and 5 °C. These prediction assays were conducted at low protein concentration (1 mg/mL). We also directly characterized high concentration (100 mg/mL) formulations for cold-induced phase separation, turbidity, and concentratibility by ultrafiltration. This systematic study allowed analysis of the correlation between the results of low concentration assays and the high concentration attributes. The key findings of this study include the following: (1) verification of the usefulness of three different parameters (Cmid, µB, and Tcloud) from PEG-induced protein precipitation assays for ranking colloidal stability of high concentration mAb formulations; (2) a new method to implement PEG-induced protein precipitation assay suitable for high throughput screening with low sample consumption; (3) improvement in the theoretical model for calculating robust thermodynamic parameters of colloidal stability (µB and εB) that are independent of specific experimental settings; (4) systematic evaluation of the effects of pH and buffer salts on colloidal stability of mAbs in common formulation buffers. These findings provide improved theoretical and practical tools for assessing the colloidal stability of mAbs and mAb-based modalities during formulation development.

Triptolide attenuates irritable bowel syndrome via inhibiting ODC1.

BACKGROUND: Irritable bowel syndrome (IBS) is a chronic disorder of the gut-brain axis with significant morbidity. Triptolide, an active compound extracted from Tripterygium wilfordii Hook F (TwHF), has been widely used as a major medicinal herb in the treatment of inflammatory disease. METHODS: The chronic-acute combined stress (CAS) stimulation was used to establish IBS rat model. The model rats were then gavaged with triptolide. Forced swimming, marble-burying, fecal weight and abdominal withdrawal reflex (AWR) score were recorded. Pathologic changes in the ileal and colonic tissues were validated by hematoxylin and eosin staining. The inflammatory cytokines and Ornithine Decarboxylase-1 (ODC1) in the ileal and colonic tissues were performed by ELISA and WB. RESULTS: Triptolide didn't have antidepressant- and antianxiety- effects in rats caused by CAS, but decreased fecal weight and AWR score. In addition, Triptolide reduced the release of IL-1, IL-6, and TNF-α and the expression of ODC1 in the ileum and colon. CONCLUSION: The therapeutic efficacy of triptolide for IBS induced by CAS was revealed in this study, which may be related to the reduction of ODC1.

Polymorphic selectivity in crystal nucleation.

Crystal nucleation rates have been measured in the supercooled melts of two richly polymorphic glass-forming liquids: ROY and nifedipine (NIF). ROY or 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile is known for its crystals of red, orange, and yellow colors and many polymorphs of solved structures (12). Of the many polymorphs, ON (orange needles) nucleates the fastest with the runner up (Y04) trailing by a factor of 103 when compared under the same mobility-limited condition, while the other unobserved polymorphs are slower yet by at least 5 orders of magnitude. Similarly, of the six polymorphs of NIF, γ' nucleates the fastest, ß' is slower by a factor of 10, and the rest are slower yet by at least 5 decades. In both systems, the faster-nucleating polymorphs are not built from the lowest-energy conformers, while they tend to have higher energies and lower densities and thus greater similarity to the liquid phase by these measures. The temperature ranges of this study covered the glass transition temperature Tg of each system, and we find no evidence that the nucleation rate is sensitive to the passage of Tg. At the lowest temperatures investigated, the rates of nucleation and growth are proportional to each other, indicating that a similar kinetic barrier controls both processes. The classical nucleation theory provides an accurate description of the observed nucleation rates if the crystal growth rate is used to describe the kinetic barrier for nucleation. The quantitative rates of both nucleation and growth for the competing polymorphs enable prediction of the overall rate of crystallization and its polymorphic outcome.

Application of machine learning model to predict lacunar cerebral infarction in elderly patients with femoral neck fracture before surgery.

BACKGROUND: Femoral neck fracture and lacunar cerebral infarction (LCI) are the most common diseases in the elderly. When LCI patients undergo a series of traumas such as surgery, their postoperative recovery results are often poor. Moreover, few studies have explored the relationship between LCI and femoral neck fracture in the elderly. Therefore, this study will develop a ML (machine learning)-based model to predict LCI before surgery in elderly patients with a femoral neck fracture. METHODS: Professional medical staff retrospectively collected the data of 161 patients with unilateral femoral neck fracture who underwent surgery in the Second Affiliated Hospital of Wenzhou Medical University database from January 1, 2015, to January 1, 2020. Patients were divided into two groups based on LCI (diagnosis based on cranial CT image): the LCI group and the non-LCI group. Preoperative clinical characteristics and preoperative laboratory data were collected for all patients. Features were selected by univariate and multivariate logistic regression analysis, with age, white blood cell (WBC), prealbumin, aspartate aminotransferase (AST), total protein, globulin, serum creatinine (Scr), blood urea nitrogen (Bun)/Scr, lactate dehydrogenase (LDH), serum sodium and fibrinogen as the features of the ML model. Five machine learning algorithms, Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Decision tree (DT), were used in combination with preoperative clinical characteristics and laboratory data to establish a predictive model of LCI in patients with a femoral neck fracture. Furthermore, indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance. RESULTS: The AUROC of 5 ML models ranged from 0.76 to 0.95. It turned out that the RF model demonstrated the highest performance in predicting LCI for femoral neck fracture patients before surgery, whose AUROC was 0.95, sensitivity 1.00, specificity 0.81, and accuracy 0.90 in validation sets. Furthermore, the top 4 high-ranking variables in the RF model were prealbumin, fibrinogen, globulin and Scr, in descending order of importance. CONCLUSION: In this study, 5 ML models were developed and validated for patients with femoral neck fracture to predict preoperative LCI. RF model provides an excellent predictive value with an AUROC of 0.95. Clinicians can better conduct multidisciplinary perioperative management for patients with femoral neck fractures through this model and accelerate the postoperative recovery of patients.

Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study.

BACKGROUND: With rapid economic development, the world's average life expectancy is increasing, leading to the increasing prevalence of osteoporosis worldwide. However, due to the complexity and high cost of dual-energy x-ray absorptiometry (DXA) examination, DXA has not been widely used to diagnose osteoporosis. In addition, studies have shown that the psoas index measured at the third lumbar spine (L3) level is closely related to bone mineral density (BMD) and has an excellent predictive effect on osteoporosis. Therefore, this study developed a variety of machine learning (ML) models based on psoas muscle tissue at the L3 level of unenhanced abdominal computed tomography (CT) to predict osteoporosis. METHODS: Medical professionals collected the CT images and the clinical characteristics data of patients over 40 years old who underwent DXA and abdominal CT examination in the Second Affiliated Hospital of Wenzhou Medical University database from January 2017 to January 2021. Using 3D Slicer software based on horizontal CT images of the L3, the specialist delineated three layers of the region of interest (ROI) along the bilateral psoas muscle edges. The PyRadiomics package in Python was used to extract the features of ROI. Then Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) algorithm were used to reduce the dimension of the extracted features. Finally, six machine learning models, Gaussian naïve Bayes (GNB), random forest (RF), logistic regression (LR), support vector machines (SVM), Gradient boosting machine (GBM), and Extreme gradient boosting (XGBoost), were applied to train and validate these features to predict osteoporosis. RESULTS: A total of 172 participants met the inclusion and exclusion criteria for the study. 82 participants were enrolled in the osteoporosis group, and 90 were in the non-osteoporosis group. Moreover, the two groups had no significant differences in age, BMI, sex, smoking, drinking, hypertension, and diabetes. Besides, 826 radiomic features were obtained from unenhanced abdominal CT images of osteoporotic and non-osteoporotic patients. Five hundred fifty radiomic features were screened out of 826 by the Mann-Whitney U test. Finally, 16 significant radiomic features were obtained by the LASSO algorithm. These 16 radiomic features were incorporated into six traditional machine learning models (GBM, GNB, LR, RF, SVM, and XGB). All six machine learning models could predict osteoporosis well in the validation set, with the area under the receiver operating characteristic (AUROC) values greater than or equal to 0.8. GBM is more effective in predicting osteoporosis, whose AUROC was 0.86, sensitivity 0.70, specificity 0.92, and accuracy 0.81 in validation sets. CONCLUSION: We developed six machine learning models to predict osteoporosis based on psoas muscle images of abdominal CT, and the GBM model had the best predictive performance. GBM model can better help clinicians to diagnose osteoporosis and provide timely anti-osteoporosis treatment for patients. In the future, the research team will strive to include participants from multiple institutions to conduct external validation of the ML model of this study.

Based on CT at the third lumbar spine level, the skeletal muscle index and psoas muscle index can predict osteoporosis.

BACKGROUND: With the increasing number of studies on osteoporosis and muscle adipose tissue, existing studies have shown that skeletal muscle tissue and adipose tissue are closely related to osteoporosis by dual-energy x-ray absorptiometry (DXA) measurement. However, few studies have explored whether the skeletal muscle and adipose tissue index measured at the lumbar spine 3 (L3) level are closely related to bone mineral density (BMD) and can even predict osteoporosis. Therefore, this study aimed to prove whether skeletal muscle and adipose tissue index measured by computed tomography (CT) images based on a single layer are closely related to BMD. METHODS: A total of 180 participants were enrolled in this study to obtain skeletal muscle index (SMI), psoas muscle index (PMI), subcutaneous fat index (SFI), visceral fat index (VFI), and the visceral-to-subcutaneous ratio of the fat area (VSR) at L3 levels and divide them into osteoporotic and normal groups based on the T-score of DXA. Spearman rank correlation was used to analyze the correlation between SMI, PMI, SFI, VFI, VSR, and BMD. Similarly, spearman rank correlation was also used to analyze the correlation between SMI, PMI, SFI, VFI, VSR, and the fracture risk assessment tool (FRAX). Receiver operating characteristic (ROC) was used to analyze the efficacy of SMI, PMI, SFI, VFI, and VSR in predicting osteoporosis. RESULTS: BMD of L1-4 was closely correlated with SMI, PMI, VFI and VSR (r = 0.199 p = 0.008, r = 0.422 p < 0.001, r = 0.253 p = 0.001, r = 0.310 p < 0.001). BMD of the femoral neck was only correlated with PMI and SFI (r = 0.268 p < 0.001, r = - 0.164 p-0.028). FRAX (major osteoporotic fracture) was only closely related to PMI (r = - 0.397 p < 0.001). FRAX (hip fracture) was closely related to SMI and PMI (r = - 0.183 p = 0.014, r = - 0.353 p < 0.001). Besides, FRAX (major osteoporotic fracture and hip fracture) did not correlate with VFI, SFI, and VSR. SMI and PMI were statistically significant, with the area under the curve (AUC) of 0.400 (95% confidence interval 0.312-0.488 p = 0.024) and 0.327 (95% confidence interval 0.244-0.410 p < 0.001), respectively. VFI, SFI, and VSR were not statistically significant in predicting osteoporosis. CONCLUSIONS: This study demonstrated that L3-based muscle index could assist clinicians in the diagnosis of osteoporosis to a certain extent, and PMI is superior to SMI in the diagnosis of osteoporosis. In addition, VFI, SFI, and VSR do not help clinicians to diagnose osteoporosis well.

Development of a nomogram to predict surgical site infection after closed comminuted calcaneal fracture.

BACKGROUND: Compared with open comminuted calcaneal fractures, less emphasis is placed on postoperative surgical site infection (SSI) of closed comminuted calcaneal fractures. This study aimed to identify the risk factors associated with SSI and build a nomogram model to visualize the risk factors for postoperative SSI. METHODS: We retrospectively collected patients with closed comminuted calcaneal fractures from the Second Affiliated Hospital of Wenzhou Medical University database from 2017 to 2020. Risk factors were identified by logistics regression analysis, and the predictive value of risk factors was evaluated by ROC (receiver operating characteristic curve). Besides, the final risk factors were incorporated into R4.1.2 software to establish a visual nomogram prediction model. RESULTS: The high-fall injury, operative time, prealbumin, aspartate aminotransferase (AST), and cystatin-C were independent predictors of SSI in calcaneal fracture patients, with OR values of 5.565 (95%CI 2.220-13.951), 1.044 (95%CI 1.023-1.064), 0.988 (95%CI 0.980-0.995), 1.035 (95%CI 1.004-1.067) and 0.010 (95%CI 0.001-0.185) (Ps < 0.05). Furthermore, ROC curve analysis showed that the AUC values of high-fall injury, operation time, prealbumin, AST, cystatin-C, and their composite indicator for predicting SSI were 0.680 (95%CI 0.593-0.766), 0.756 (95%CI 0.672-939), 0.331 (95%CI 0.243-0.419), 0.605 (95%CI 0.512-0.698), 0.319 (95%CI 0.226-0.413) and 0.860 (95%CI 0.794-0.926), respectively (Ps < 0.05). Moreover, the accuracy of the nomogram to predict SSI risk was 0.860. CONCLUSIONS: Our study findings suggest that clinicians should pay more attention to the preoperative prealbumin, AST, cystatin C, high-fall injury, and operative time for patients with closed comminuting calcaneal fractures to avoid the occurrence of postoperative SSI. Furthermore, our established nomogram to assess the risk of SSI in calcaneal fracture patients yielded good accuracy and can assist clinicians in taking appropriate measures to prevent SSI.

A nomogram for predicting post-operative wound complications after open reduction and internal fixation for calcaneal fractures.

The purpose of our study was to determine the risk factors for post-operative wound complications (PWCs) after open reduction and internal fixation (ORIF) for calcaneal fracture and establish a nomogram prediction model. We retrospectively analysed the clinical data of patients who suffered from calcaneal fractures and had been surgically treated for ORIF in our institution between January 2010 and January 2020. Perioperative information was obtained through the electronic medical record system, univariate and multivariate analyses were performed to determine the risk factors of PWCs, and a nomogram model was constructed to predict the risk of PWCs. The predictive performance and consistency of the model were evaluated by the Hosmer -Lemeshow (H-L) test and the calibration curve. In total, 444 patients were enrolled in our study. Multivariate analysis results showed that smoking, limb swelling, angle of incision, and CRP were independent risk factors for skin necrosis. The AUC value for skin necrosis risk was 0.982 (95%CI 0.97-0.99). The H-L test revealed that the normogram prediction model had good calibration ability (P = .957). Finally, we found a correlation between PWCs and smoking, limb swelling, angle of incision, and CRP after ORIF for calcaneal fracture patients. Our nomogram prediction model might be helpful for clinicians to identify high-risk patients, as interventions could be taken early to reduce the incidence of PWCs.

Structures of glass-forming liquids by x-ray scattering: Glycerol, xylitol, and D-sorbitol.

Synchrotron x-ray scattering has been used to investigate three liquid polyalcohols of different sizes (glycerol, xylitol, and D-sorbitol) from above the glass transition temperatures Tg to below. We focus on two structural orders: the association of the polar OH groups by hydrogen bonds (HBs) and the packing of the non-polar hydrocarbon groups. We find that the two structural orders evolve very differently, reflecting the different natures of bonding. Upon cooling from 400 K, the O⋯O correlation at 2.8 Å increases significantly in all three systems, indicating more HBs, until kinetic arrests at Tg; the increase is well described by an equilibrium between bonded and non-bonded OH with ΔH = 9.1 kJ/mol and ΔS = 13.4 J/mol/K. When heated above Tg, glycerol loses the fewest HBs per OH for a given temperature rise scaled by Tg, followed by xylitol and by D-sorbitol, in the same order the number of OH groups per molecule increases (3, 5, and 6). The pair correlation functions of all three liquids show exponentially damped density modulations of wavelength 4.5 Å, which are associated with the main scattering peak and with the intermolecular C⋯C correlation. In this respect, glycerol is the most ordered with the most persistent density ripples, followed by D-sorbitol and by xylitol. Heating above Tg causes faster damping of the density ripples with the rate of change being the slowest in xylitol, followed by glycerol and by D-sorbitol. Given the different dynamic fragility of the three liquids (glycerol being the strongest and D-sorbitol being the most fragile), we relate our results to the current theories of the structural origin for the difference. We find that the fragility difference is better understood on the basis of the thermal stability of HB clusters than that of the structure associated with the main scattering peak.

Atomic-Level Drug Substance and Polymer Interaction in Posaconazole Amorphous Solid Dispersion from Solid-State NMR.

Despite the wide utilization of amorphous solid dispersions (ASDs) for formulating poorly water-soluble drugs, fundamental understanding of the structural basis behind their stability and dissolution behavior is limited. This is largely due to the lack of high-resolution structural tools for investigating multicomponent and amorphous systems in the solid state. In this study, we present what is likely the first publication quantifying the molecular interaction between the drug and polymer in ASDs at an angstrom level by utilizing 19F magic angle spinning (MAS) nuclear magnetic resonance (NMR) techniques. A variant of the 19F-13C rotational-echo and double-resonance (REDOR) technique was developed to quantify interatomic distances by implementing a supercycled symmetry-based recoupling schedule and synchronized simultaneous detection. We successfully deployed the technique to identify "head-to-head" and "head-to-tail" packing of crystalline posaconazole (POSA). To probe molecular interactions between POSA and hypromellose acetate succinate (HPMCAS) in the dispersion, as a major goal of this study, two-dimensional (2D) 1H-19F correlation experiments were performed. The approach facilitated observation of intermolecular hydrogen-to-fluorine contacts between the hydroxyl group of the polymer and the difluorophenyl group of the drug substance. Atomic distance measurement, utilizing the developed 19F-13C REDOR technique, revealed the close proximity of 13COH-19F at 4.3 Å. Numerical modeling analysis suggested a possible hydrogen bonding interaction between the polymer O-H group as an acceptor and POSA fluorine (O-H···F) or difluorophenyl ring (O-H···Ph) as a donor. These 19F MAS NMR techniques, including 2D 19F-1H heteronuclear correlation and 19F-13C atomic distance measurement, may shed light on the nature (i.e., type and strength) of drug-polymer interactions in ASDs and offer a new high-resolution analytical protocol for probing the microstructure of amorphous pharmaceutical materials.

Surface diffusion in glasses of rod-like molecules posaconazole and itraconazole: effect of interfacial molecular alignment and bulk penetration.

The method of surface grating decay has been used to measure surface diffusion in the glasses of two rod-like molecules posaconazole (POS) and itraconazole (ITZ). Although structurally similar antifungal medicines, ITZ forms liquid-crystalline phases while POS does not. Surface diffusion in these systems is significantly slower than in the glasses of quasi-spherical molecules of similar volume when compared at the glass transition temperature Tg. Between the two systems, ITZ has slower surface diffusion. These results are explained on the basis of the near-vertical orientation of the rod-like molecules at the surface and their deep penetration into the bulk where mobility is low. For molecular glasses without extensive hydrogen bonds, we find that the surface diffusion coefficient at Tg decreases smoothly with the penetration depth of surface molecules and the trend has the double-exponential form for the surface mobility gradient observed in simulations. This supports the view that these molecular glasses have a similar mobility vs. depth profile and their different surface diffusion rates arise simply from the different depths at which molecules are anchored. Our results also provide support for a previously observed correlation between the rate of surface diffusion and the fragility of the bulk liquid.

Probing the Molecular-Level Interactions in an Active Pharmaceutical Ingredient (API) - Polymer Dispersion and the Resulting Impact on Drug Product Formulation.

PURPOSE: An investigation of underlying mechanisms of API-polymer interaction patterns has the potential to provide valuable insights for selecting appropriate formulations with superior physical stability and processability. MATERIALS AND METHODS: In this study, copovidone was used as a polymeric carrier for several model compounds including clotrimazole, nifedipine, and posaconazole. The varied chemical structures conferred the ability for the model compounds to form distinct interactions with copovidone. Rheology and nuclear magnetic resonance (NMR) were combined to investigate the molecular pattern and relative strength of active pharmaceutical ingredient (API)-polymer interactions. In addition, the impact of the interactions on formulation processability via hot melt extrusion (HME) and physical stability were evaluated. RESULTS: The rheological response of an API-polymer system was found to be highly sensitive to API-polymer interaction, depending both on API chemistry and API-polymer miscibility. In the systems studied, dispersed API induced a stronger plasticizer effect on the polymer matrix compared to crystalline/aggregated API. Correspondingly, the processing torque via HME showed a proportional relationship with the maximum complex viscosity of the API-polymer system. In order to quantitatively evaluate the relative strength of the API-polymer interaction, homogeneously dispersed API-polymer amorphous samples were prepared by HME at an elevated temperature. DSC, XRD, and rheology were employed to confirm the amorphous integrity and homogeneity of the resultant extrudates. Subsequently, the homogeneously dispersed API-polymer amorphous dispersions were interrogated by rheology and NMR to provide a qualitative and quantitative assessment of the nature of the API-polymer interaction, both macroscopically and microscopically. Rheological master curves of frequency sweeps of the extrudates exhibited a strong dependence on the API chemistry and revealed a rank ordering of the relative strength of API-copovidone interactions, in the order of posaconazole > nifedipine > clotrimazole. NMR data provided the means to precisely map the API-polymer interaction pattern and identify the specific sites of interaction from a molecular perspective. Finally, the impact of API-polymer interactions on the physical stability of the resultant extrudates was studied. CONCLUSION: Qualitative and quantitative evaluation of the relative strength of the API-polymer interaction was successfully accomplished by utilizing combined rheology and NMR. Graphical Abstract.

Molecular packing of pharmaceuticals analyzed with paramagnetic relaxation enhancement and ultrafast magic angle pinning NMR.

Understanding the relationship between the structure and the physicochemical attributes of crystalline pharmaceuticals requires high-resolution molecular details. Solid-state nuclear magnetic resonance (ssNMR) spectroscopy is an indispensable tool for analyzing molecular structures, but often experiences challenges of low spectral resolution and sensitivity, particularly in the characterization of unlabeled pharmaceutical materials. Besides, the relatively long spin-lattice relaxation times in pharmaceutical crystals result in time-consuming data collections. In this study, we utilize ultrafast magic angle spinning (UF-MAS) of the sample at 60 and 110 kHz to enable proton and fluorine spectroscopies for probing the structural details of crystalline posaconazole. Paramagnetic relaxation enhancement (PRE), obtained by doping Cu(ii) ions into the crystalline lattice and coating on particle surface, is implemented to shorten the spin-lattice relaxation time for speeding up the ssNMR acquisition. Our results demonstrate a remarkably improved 1H and 19F resolution and sensitivity, which enables multi-dimensional 1H-1H and heteronuclear 1H-19F correlations. In combination with density functional theory (DFT) calculations of chemical shifts, molecular details of posaconazole are established in terms of 1H and 19F networks for identifying "head-to-tail" and "head-to-head" intermolecular packings, with presumably critical contacts that stabilize the crystalline structure. The PRE and UF-MAS techniques enable the high-resolution structure characterization of fluorinated drug molecules in pharmaceutical formulations at natural abundance.

In situ solid-state NMR characterization of pharmaceutical materials: An example of drug-polymer thermal mixing.

Pharmaceutical amorphous solid dispersions, a multicomponent system prepared by dispersing drug substances into polymeric matrix via thermal and mechanical processes, represent a major platform to deliver the poorly water-soluble drug. Microscopic properties of drug-polymer contacts play mechanistic roles in manipulating long-term physical stability as well as dissolution profiles. Although solid-state nuclear magnetic resonance has been utilized as an indispensable tool to probe structural details, previous studies are limited to ex situ characterizations. Our work provides likely the first documented example to investigate comelting of ketoconazole and polyacrylic acid, as a model system, in an in situ manner. Their physical mixture is melted and mixed in the solid-state nuclear magnetic resonance rotor under magic angle spinning at up to approximately 400 K. Critical structural events of molecular miscibility and interaction have been successfully identified. These results design and evaluate the instrumental and experimental protocols for real-time characterizations of the comelting of pharmaceutical materials.

Understanding Compression-Induced Amorphization of Crystalline Posaconazole.

Process-induced phase transformations (PIPTs) of active pharmaceutical ingredients (APIs) can alter APIs' physicochemical properties and impact performance of pharmaceutical drug products. In this study, we investigated compression-induced amorphization of crystalline posaconazole (POSA), where the impact of mechanical stresses and excipients on amorphization were explored. 19F solid-state NMR (ssNMR) was utilized to detect and quantify amorphous content in the compressed tablets, and finite element analysis (FEA) was conducted to understand stress distributions in the compression process. Both applied macroscopic axial stress and shear stress were found to be important to amorphization of crystalline POSA. Punch velocity, an important compression process parameter, had negligible effect on amorphization up to 100 mm/s. Two diluents, microcrystalline cellulose (MCC) and dibasic calcium phosphate anhydrous (DCPA), and one lubricant, magnesium stearate (MgSt), were evaluated for their impact on amorphization in this study. It was found that both MCC and DCPA significantly enhanced amorphization of POSA at a low drug loading (5% w/w). The 1% (w/w) blended lubricant effectively reduced the amorphous content in MCC-POSA tablets; however, it had minimal effect on either neat POSA or DCPA-POSA tablets. Drug loading, or excipient concentration, was demonstrated to have a significant impact on the extent of amorphization. These observed excipient effects support the important role of interparticulate stresses in amorphization of crystalline POSA.

Molecular Interactions in Posaconazole Amorphous Solid Dispersions from Two-Dimensional Solid-State NMR Spectroscopy.

Molecular interactions between the active pharmaceutical ingredient and polymer have potentially substantial impacts on the physical stability of amorphous solid dispersions (ASDs), presumably by manipulating molecular mobility and miscibility. However, structural details for understanding the nature of the molecular contacts and mechanistic roles in various physicochemical and thermodynamic events often remain unclear. This study provides a spectroscopic characterization of posaconazole (POSA) formulations, a second-generation triazole antifungal drug (Noxafil, Merck & Co., Inc., Kenilworth, NJ, USA), at molecular resolution. One- and two-dimensional (2D) solid-state NMR (ssNMR) techniques including spectral editing, heteronuclear 1H-13C, 19F-13C, 15N-13C, and 19F-1H polarization transfer, and spin correlation and ultrafast magic angle spinning, together with the isotopic labeling strategy, were utilized to uncover molecular details in POSA ASDs in a site-specific manner. Active groups in triazole and difluorophenyl rings exhibited rich but distinct categories of interactions with two polymers, hypromellose acetate succinate and hypromellose phthalate, including intermolecular O-H···O═C and O-H···F-C hydrogen bonding, π-π aromatic packing, and electrostatic interaction. Interestingly, the chlorine-to-fluorine substituent in POSA, one of the major structural differences from itraconazole that could facilitate binding to the biological target, offers an additional contact with the polymer. These findings exhibit 2D ssNMR as a sensitive technique for probing sub-nanometer structures of pharmaceutical materials and provide a structural basis for optimizing the type and strength of drug-polymer interactions in the design of amorphous formulations.

Organic Glasses with Tunable Liquid-Crystalline Order.

Liquid crystals (LCs) are known to undergo rapid ordering transitions with virtually no hysteresis. We report a remarkable counterexample, itraconazole, where the nematic to smectic transition is avoided at a cooling rate exceeding 20 K/s. The smectic order trapped in a glass is the order reached by the equilibrium liquid before the kinetic arrest of the end-over-end molecular rotation. This is attributed to the fact that smectic ordering requires orientational ordering and suggests a general condition for preparing organic glasses with tunable LC order for electronic applications.

Surface Enrichment and Depletion of the Active Ingredient in Spray Dried Amorphous Solid Dispersions.

PURPOSE: To study the effects of physicochemical properties of drug and polymer, as well as the drug-polymer interactions, on the surface composition of SDDs. METHODS: Ethanol solutions containing a model drug (IMC, NMP or FCZ) and a model polymer (PVPK12, PVPK30 or PVP-VA) were spray dried, and the surface composition of SDDs was analyzed by XPS. The surface tensions of pure components and their solutions were measured using Wilhelmy plate and/or calculated using ACD/Labs. NMR and DLS were used to obtain the diffusion coefficients of IMC, NMP, PVPK12 and PVPK30 in solvents. Flory-Huggins interaction parameters for selected drug-polymer pairs were obtained using a melting point depression method. RESULTS: Significant surface enrichment or depletion of the drug was observed in SDDs depending on the particular drug-polymer combination. With PVP as the dispersion polymer, IMC and NMP were surface enriched; whereas FCZ, a hydrophilic drug, was surface depleted. With increasing PVP molecular weight, the surface drug concentration increased, and the effect was greater in the NMP/PVP and FCZ/PVP systems than in the IMC/PVP system where strong drug-polymer interaction existed. Changing the polymer from PVP to PVP-VA reduced the surface concentration of the drug. CONCLUSIONS: The surface concentration of a SDD can be significantly different from the bulk concentration. The main results of this work are consistent with the notion that the relative surface tensions control surface enrichment or depletion. Besides, the relative diffusion rates of the components and the strength of their interactions may also affect the surface composition of the SDDs.

Crystal nucleation rates in glass-forming molecular liquids: D-sorbitol, D-arabitol, D-xylitol, and glycerol.

The rate of crystal nucleation has been measured in four glass-forming molecular liquids: D-sorbitol, D-arabitol, D-xylitol, and glycerol. These polyalcohols have similar rates of crystal growth when compared at the same temperature relative to Tg (the glass transition temperature), peaking near 1.4 Tg, while the nucleation rates J are vastly different. In D-sorbitol and D-arabitol, J reaches a maximum of ∼108 m-3 s-1 near 1.1 Tg, whereas J < 10-2 m-3 s-1 in D-xylitol and <1 m-3 s-1 in glycerol based on no nucleation in large samples after long waits. This confirms the fundamentally different mechanisms for nucleation and growth. Near Tg, both nucleation and growth slow down with a similar temperature dependence, suggesting a similar kinetic barrier for the two processes. This temperature dependence is significantly weaker than that of viscosity η, approximately following η-0.75. This indicates that viscosity is a poor representative of the kinetic barrier for nucleation, and a better choice is the crystal growth rate. Under the latter assumption, the classical nucleation theory (CNT) describes our data reasonably well, yielding σ = 0.013 J/m2 for D-sorbitol and 0.026 J/m2 for D-arabitol, where σ is the critical nucleus/liquid interfacial free energy. There is no strong indication that the CNT fails as the length scale for corporative rearrangement exceeds the size of the critical nucleus, as recently suggested for lithium disilicate.