Early Catastrophic Failure of Cervical Disc Arthroplasty: A Case Report

CASE

A 47-year-old healthy man underwent cervical disc arthroplasty (CDA) for a C6 radiculopathy. Two-week radiographs showed a well-positioned implant. At the 6-week postoperative visit, the inferior portion of the implant had displaced ventrally, with C6 anterior vertebral body collapse. The next day, the implant was removed and converted to a C5/6 anterior cervical discectomy and fusion. Bone biopsy was unremarkable.

CONCLUSIONS

This is the first reported case of early catastrophic failure of a well-positioned CDA in a healthy patient with good bone quality. Possible contributing mechanisms include hypermobility and anterior bone loss, factors previously associated with CDA.

Insula and orbitofrontal cortical morphology in substance dependence is modulated by sex

BACKGROUND AND PURPOSE

Frontolimbic circuits are involved in learning and decision-making processes thought to be affected in substance-dependent individuals. We investigated frontolimbic cortical morphometry in substance-dependent men and women and determined whether morphometric measurements correlated with decision-making performance.

MATERIALS AND METHODS

Twenty-eight abstinent SDI (17 men/11 women) were compared with 28 controls (13 men/15 women). Cortical thicknesses and volumes were computed by using FreeSurfer. After controlling for age and intracranial volume, group and sex effects were analyzed in 3 a priori regions of interest: the insula, orbitofrontal cortex, and anterior cingulate cortex by using analysis of covariance. A secondary whole-brain analysis was conducted to verify region-of-interest results and to explore potential differences in other brain regions.

RESULTS

Region-of-interest analyses revealed a main effect of group on the left insula cortex, which was thinner in SDI compared with controls (P = .02). There was a group by sex interaction on bilateral insula volume (left, P = .02; right, P = .001) and right insula cortical thickness (P = .007). Compared with same-sex controls, female SDI had smaller insulae, whereas male SDI had larger insulae. Neither ACC nor OFC significantly differed across group. Performance on a decision-making task was better in controls than SDI and correlated with OFC measurements in the controls.

CONCLUSIONS

SDI and controls differed in insula morphology, and those differences were modulated by sex. No group differences in OFC were observed, but OFC measurements correlated with negative-reinforcement learning in controls. These preliminary results are consistent with a hypothesis that frontolimbic pathways may be involved in behaviors related to substance dependence.

Modelling formulations using gene expression programming--a comparative analysis with artificial neural networks

This study has investigated the utility and potential advantages of gene expression programming (GEP)--a new development in evolutionary computing for modelling data and automatically generating equations that describe the cause-and-effect relationships in a system--to four types of pharmaceutical formulation and compared the models with those generated by neural networks, a technique now widely used in the formulation development. Both methods were capable of discovering subtle and non-linear relationships within the data, with no requirement from the user to specify the functional forms that should be used. Although the neural networks rapidly developed models with higher values for the ANOVA R(2) these were black box and provided little insight into the key relationships. However, GEP, although significantly slower at developing models, generated relatively simple equations describing the relationships that could be interpreted directly. The results indicate that GEP can be considered an effective and efficient modelling technique for formulation data.

Effect of crystallisation conditions and feedstock morphology on the aerosolization performance of micronised salbutamol sulphate

Salbutamol sulphate (SS) used in dry powder inhalers requires drug particles in the respirable size range of 1-5 μm to achieve a suitable therapeutic effect. The aim of this study was therefore to determine strategies for controlling drug substance characteristics pre and post-crystallisation to facilitate the production of micronised SS with desirable particle attributes for optimal delivery as an inhaled aerosol. SS batches were crystallised using an antisolvent method to produce a range of crystal morphologies. Air jet milling was then used to reduce the size of crystallised SS particles. Starting materials and micronised batches of SS were characterised in the solid state using a range of techniques with subsequent assessment of aerosol properties. Assessment of the aerodynamic characteristics of micronised SS delivered by DPI (without any carrier) indicated that fine particle fraction and emitted dose as a percentage of the total recovered dose were dependent on the quality attributes of the micronised SS, which were directly linked to the degree of imperfections and the morphology of the crystalline feedstock used in micronisation. Aerosolization performance of micronised SS can be optimised by manipulation of feedstock characteristics through crystal engineering and through definition of optimal processing conditions for micronisation.

Comminution of ibuprofen to produce nano-particles for rapid dissolution

A critical problem associated with poorly soluble drugs is low and variable bioavailability derived from slow dissolution and erratic absorption. The preparation of nano-formulations has been identified as an approach to enhance the rate and extent of drug absorption for compounds demonstrating limited aqueous solubility. A new technology for the production of nano-particles using high speed, high efficiency processes that can rapidly generate nano-particles with rapid dissolution rate has been developed. Size reduction of a low melting ductile model compound was achieved in periods less than 1h. Particle size reduction of ibuprofen using this methodology resulted in production of crystalline particles with average diameter of approximately 270nm. Physical stability studies showed that the nano-suspension remained homogeneous with slight increases in mean particle size, when stored at room temperature and under refrigerated storage conditions 2-8°C for up to 2 days. Powder containing crystalline drug was prepared by spray-drying ibuprofen nano-suspensions with mannitol dissolved in the aqueous phase. Dissolution studies showed similar release rates for the nano-suspension and powder which were markedly improved compared to a commercially available drug product. Ibuprofen nano-particles could be produced rapidly with smaller sizes achieved at higher suspension concentrations. Particles produced in water with stabilisers demonstrated greatest physical stability, whilst rapid dissolution was observed for the nano-particles isolated in powder form.

Advantages of neurofuzzy logic against conventional experimental design and statistical analysis in studying and developing direct compression formulations

This study has investigated the utility and potential advantages of an artificial intelligence technology - neurofuzzy logic - as a modeling tool to study direct compression formulations. The modeling performance was compare with traditional statistical analysis. From results it can be stated that the normalized error obtained from neurofuzzy logic was lower. Compared to the multiple regression analysis neurofuzzy logic showed higher accuracy in prediction for the five outputs studied. Rule sets generated by neurofuzzy logic are completely in agreement with the findings based on statistical analysis and advantageously generate understandable and reusable knowledge. Neurofuzzy logic is easy and rapid to apply and outcomes provided knowledge not revealed via statistical analysis.

Virtual reality in paediatric rehabilitation: a review

OBJECTIVE

To provide a narrative review of studies regarding the outcomes of Virtual Reality (VR)-based treatment and rehabilitation programmes within the paediatric population.

METHODS

Studies related to the use of VR across a number of paediatric areas (e.g. cerebral palsy, autism, foetal alcohol syndrome and attention deficits) were identified and summarized.

RESULTS

Outcomes from the studies reviewed provide preliminary support for the use of VR.

CONCLUSION

VR may be an effective treatment method for specific disorders, although the generalizability of this literature is hindered by several methodological limitations, such as small samples and the absence of appropriate control participants.

Data Mining of Fractured Experimental Data Using Neurofuzzy Logic–Discovering and Integrating Knowledge Hidden in Multiple Formulation Databases for a Fluid-Ded Granulation Process

In the pharmaceutical field, current practice in gaining process understanding by data analysis or knowledge discovery has generally focused on dealing with single experimental databases. This limits the level of knowledge extracted in the situation where data from a number of sources, so called fractured data, contain interrelated information. This situation is particularly relevant for complex processes involving a number of operating variables, such as a fluid-bed granulation. This study investigated three data mining strategies to discover and integrate knowledge "hidden" in a number of small experimental databases for a fluid-bed granulation process using neurofuzzy logic technology. Results showed that more comprehensive domain knowledge was discovered from multiple databases via an appropriate data mining strategy. This study also demonstrated that the textual information excluded in individual databases was a critical parameter and often acted as the precondition for integrating knowledge extracted from different databases. Consequently generic knowledge of the domain was discovered, leading to an improved understanding of the granulation process.

Separation processes for organic molecules using SCF Technologies

Supercritical fluids have been applied for many years for the separation of solutes from solids or solute mixtures in both exploratory and industrial applications. In the pharmaceutical industry the generation of pure solid states without impurities is important as the presence of impurities can result in a change in chemical properties or lead to physical instability. The literature on the separation and purification of solutes from solid matrices and solute mixtures using supercritical fluids, with the main emphasis on pharmaceutically important molecules, is reviewed in this article. Also discussed is the application of supercritical fluids in the control of process impurities such as chemical intermediates and residual solvent and in polymorphic control and chiral resolution. As the generation of organic molecules of pharmaceutical interest with high purity is important in pharmaceuticals this review additionally provides a brief overview of highly selective chemical reactions in supercritical fluids.

Crystal engineering of active pharmaceutical ingredients to improve solubility and dissolution rates

The increasing prevalence of poorly soluble drugs in development provides notable risk of new products demonstrating low and erratic bioavailability with consequences for safety and efficacy, particularly for drugs delivered by the oral route of administration. Although numerous strategies exist for enhancing the bioavailability of drugs with low aqueous solubility, the success of these approaches is not yet able to be guaranteed and is greatly dependent on the physical and chemical nature of the molecules being developed. Crystal engineering offers a number of routes to improved solubility and dissolution rate, which can be adopted through an in-depth knowledge of crystallisation processes and the molecular properties of active pharmaceutical ingredients. This article covers the concept and theory of crystal engineering and discusses the potential benefits, disadvantages and methods of preparation of co-crystals, metastable polymorphs, high-energy amorphous forms and ultrafine particles. Also considered within this review is the influence of crystallisation conditions on crystal habit and particle morphology with potential implications for dissolution and oral absorption.

Investigation of an artificial intelligence technology—Model trees

This study has investigated an artificial intelligence technology - model trees - as a modelling tool applied to an immediate release tablet formulation database. The modelling performance was compared with artificial neural networks that have been well established and widely applied in the pharmaceutical product formulation fields. The predictability of generated models was validated on unseen data and judged by correlation coefficient R(2). Output from the model tree analyses produced multivariate linear equations which predicted tablet tensile strength, disintegration time, and drug dissolution profiles of similar quality to neural network models. However, additional and valuable knowledge hidden in the formulation database was extracted from these equations. It is concluded that, as a transparent technology, model trees are useful tools to formulators.

Comparison of neurofuzzy logic and decision trees in discovering knowledge from experimental data of an immediate release tablet formulation

Understanding of the cause-effect relationships between formulation ingredients, process conditions and product properties is essential for developing a quality product. However, the formulation knowledge is often hidden in experimental data and not easily interpretable. This study compares neurofuzzy logic and decision tree approaches in discovering hidden knowledge from an immediate release tablet formulation database relating formulation ingredients (silica aerogel, magnesium stearate, microcrystalline cellulose and sodium carboxymethylcellulose) and process variables (dwell time and compression force) to tablet properties (tensile strength, disintegration time, friability, capping and drug dissolution at various time intervals). Both approaches successfully generated useful knowledge in the form of either "if then" rules or decision trees. Although different strategies are employed by the two approaches in generating rules/trees, similar knowledge was discovered in most cases. However, as decision trees are not able to deal with continuous dependent variables, data discretisation procedures are generally required.

The application of molecular modelling to the interpretation of inverse gas chromatography data

The use of molecular modelling in the interpretation of inverse gas chromatography data is discussed. Crystal faces can be visualised and likely cleavage planes calculated using the surface attachment energies. Assuming that the preferred cleavage plane is the crystal face with the smallest attachment energy then the predominant crystal faces of a crystalline particle can be predicted. Surface adsorption can be modelled using Van der Waals and electrostatic interactions to evaluate the interaction energies between individual atoms of the probe molecule and atoms of the test molecule orientated as in the surface. Using examples of pharmaceutical materials, modelling has been shown to be successful in the understanding of changes in the surface energetics.

Process control and scale-up of pharmaceutical wet granulation processes: a review

In this paper the techniques for process control and scale-up of pharmaceutical wet granulation processes are reviewed. For wet granulation in high-shear mixers, specific methods based on the liquid saturation and the consistency of the wet mass are described. Both parameters can be used to quantify the deformability of the wet granules, and relate well with the particle size of the end granules. In practice, the power consumption of the high-shear mixer is used for the monitoring of the wet granulation process, whilst for scale-up, it is helpful to use the underlying relationship between power consumption and saturation level or wet mass consistency. In fluid bed granulation the granulation process is different and the moisture content in the bed is the key parameter to control. This can be monitored directly by near infrared probes or indirectly with temperature probes. As a large number of inter-related variables can be adjusted to modify the process, computerized techniques have become popular for fluid-bed process control--fuzzy logic, neural networks, and models based on experimental design techniques are several examples. In addition, engineering techniques based on particle size population balance modelling are under development for both fluid bed and high-shear granulation.

Solubility and precipitation of nicotinic acid in supercritical carbon dioxide

Solubilities of a model compound (nicotinic acid) in pure supercritical carbon dioxide (SC-CO(2)) and SC-CO(2) modified with methanol have been measured in the pressure range of 80-200 bar and between temperatures of 35 and 90 degrees C. On-line ultraviolet detection enabled a simple and relatively fast measurement of very low levels of solubility (10(-7) mol fraction) with good accuracy in pure and modified SC-CO(2). The solute solubility in both pure SC-CO(2) and SC-CO(2) modified with methanol increased with pressure at all investigated temperatures. A retrograde solubility behavior was observed in that, at pressures below 120 bar, a solubility decrease on temperature increase occurred. Solubility data were used to calculate supersaturation values and to define optimum operating conditions to obtain crystalline particles 1-5 microm in diameter using the solution-enhanced dispersion by supercritical fluids (SEDS) process, thereby demonstrating the feasibility of a one-step production process for particulate pharmaceuticals suitable for respiratory drug delivery.

Crystallization of pure anhydrous polymorphs of carbamazepine by solution enhanced dispersion with supercritical fluids (SEDS)

Pure anhydrous polymorphs of carbamazepine were prepared by solution-enhanced dispersion with supercritical fluids (SEDS). Crystallization of the polymorphs was studied. Mechanisms are proposed that consider the thermodynamics of carbamazepine, supersaturation in the SEDS process, and the binary phase equilibria of organic solvents and the carbon dioxide antisolvent. alpha-Carbamazepine was crystallized at high supersaturations and low temperatures, beta-carbamazepine crystallized from a methanol-carbon dioxide phase split, and gamma-carbamazepine crystallized via nucleation at high temperatures and low supersaturation.

Time-resolved X-ray scattering using synchrotron radiation applied to the study of a polymorphic transition in carbamazepine

The thermodynamic status of alpha-carbamazepine has been clarified using equilibrium solubility measurements, differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), heated X-ray powder diffraction (XRPD), and temperature-controlled X-ray scattering techniques. alpha-Carbamazepine is the least stable of the three well-characterized anhydrous polymorphs of carbamazepine at 25 degrees C. In addition, it was confirmed that alpha-carbamazepine undergoes an exothermic transition to gamma-carbamazepine at 130 degrees C. The novel technique of time-resolved simultaneous small- and wide-angle X-ray scattering has been successfully applied to monitor this transition in situ. It was concluded that alpha-carbamazepine has a monotropic relationship with gamma-carbamazepine.

Characterization of two polymorphs of salmeterol xinafoate crystallized from supercritical fluids

PURPOSE

To characterize two polymorphs of salmeterol xinafoate (SX-I and SX-II) produced by supercritical fluid crystallization.

METHODS

SX-I and SX-II were crystallized as fine powders using Solution Enhanced Dispersion by Supercritical Fluids (SEDS). The two polymorphs and a reference micronized SX sample (MSX) were characterized using powder X-ray diffractometry (PXRD), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), aqueous solubility (and dissolution) determination at 5-40 degrees C, BET adsorption analysis, and inverse gas chromatography (IGC).

RESULTS

Compared with SX-I, SX-II exhibited a lower enthalpy of fusion, a higher equilibrium solubility, a higher intrinsic dissolution rate, a lower enthalpy of solution (based on van't Hoff solubility plots), and a different FTIR spectrum (reflecting differences in intermolecular hydrogen bonding). Solubility ratio plot yielded a transition temperature (-99 degrees C) below the melting points of both polymorphs. MSX showed essentially the same crystal form as SX-I (confirmed by PXRD and FTIR), but a distinctly different thermal behaviour. Mild trituration of SX-I afforded a similar DSC profile to MSX while prolonged grinding of SX-I gave rise to an endotherm at -109 degrees C, corresponding to solid-solid transition of SX-I to SX-II. Surface analysis of MSX, SX-I, and SX-II by IGC revealed significant differences in surface free energy in terms of both dispersive (nonpolar) interactions and specific (polar) acid-base properties.

CONCLUSIONS

The SEDS-processed SX-I and SX-II display high polymorphic purity and distinctly different physical and surface properties. The polymorphs are related enantiotropically with SX-I being the thermodynamically stable form at room temperature.

The changes in surface energetics with relative humidity of carbamazepine and paracetamol as measured by inverse gas chromatography

The surface energetic parameters of carbamazepine and paracetamol have been studied using inverse gas chromatography modified to produce dry and ambient conditions within the column. The values of the dispersive component of the surface free energy (gamma(S)D) do not change significantly at the increased relative humidity. In contrast the specific component of the free energy of adsorption (-DeltaG(A)SP) as measured by polar probes, can either remain constant or decrease by up to 10%, depending on the material and the probe. This indicates that an increase in the relative humidity causes a decrease in the surface energetics of the powder surface. It is proposed that where the water molecules are adsorbing to the same sites as the polar probes, the interaction of these probes with the surface is decreased. To identify these sites, the preferential interaction of each probe, including water, with the drug molecule has been modelled.

Polymorph control of sulfathiazole in supercritical CO2

PURPOSE

Sulfathiazole was used to investigate polymorph control in liquid and supercritical CO2. Conventional techniques require a variety of solvents and techniques to produce different polymorphs. The present approach involves precipitation from an organic solution with liquid or supercritical CO2 using the SEDS process.

METHODS

Sulfathiazole was precipitated from methanol or acetone solutions. Experiments were carried out within a temperature range of 0-120 degrees C. Composition of the fluid phase was varied between x(CO2) = 0.27-0.99. Pressure was constant at 200 bar. Samples obtained were analyzed using SEM, DSC, and XRPD.

RESULTS

Pure polymorphs were obtained at different temperatures and flow rate ratios of CO2/solvent. With methanol Form I, III, and IV and their mixtures could be crystallized. With acetone Form I or a mixture of Form I and amorphous sulfathiazole was obtained. The fluid composition was used as a control parameter to define the process areas (T-x diagram) where the pure forms or mixtures of different forms could be obtained.

CONCLUSIONS

The experiments enabled the relationship between flow and temperature for each polymorph to be determined. The crystallization method developed proved to be a simple and efficient technique for reproducible and consistent isolation of sulfathiazole polymorphs.

Solubility prediction of salmeterol xinafoate in water--dioxane mixtures

The mole fraction solubility of salmeterol xinafoate was determined in various concentrations of dioxane in aqueous binary mixture. Maximum solubility was observed in 90% v/v dioxane and solubility parameter of the solute was estimated from solubility peak equal to 24.99 MPa(0.5). The predicting capability of four different cosolvency models was also evaluated employing a five data point training set. The solubility data at other cosolvent concentrations were predicted using the trained models, with percentage average errors for 28 drug solubility data sets in water-cosolvent mixtures lying between 12.5 and 15.0%. Further predictive model is proposed for accurate solubility predictions based on a minimum number of experiments. The percentage average error where tested was 10.6%.