A Multi-Platform Flow Device for Microbial (Co-) Cultivation and Microscopic Analysis
Published May 14, 2012

Matthijn C Hesselman
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Dorett I Odoni
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Brendan M Ryback
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Suzette De Groot
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Ruben G A Van Heck
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Jaap Keijsers
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Pim Kolkman
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

David Nieuwenhuijse
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Youri M Van Nuland
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Erik Sebus
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Rob Spee
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Hugo De Vries
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Marten T Wapenaar
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Colin J Ingham
MicroDish BV
The NetherlandsUtrecht

Karin Schroë
Food Process Engineering
Wageningen University
The NetherlandsWageningen

Vítor A P Martins
Dos Santos
Systems and Synthetic Biology
Wageningen University
The NetherlandsWageningen

Sebastiaan K Spaans
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Laboratory of Microbiology
Wageningen University
The NetherlandsWageningen

Floor Hugenholtz
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Laboratory of Microbiology
Wageningen University
The NetherlandsWageningen

Netherlands Consortium for Systems Biology
University of Amsterdam
AmsterdamThe Netherlands

Mark W J Van Passel
Wageningen UR iGEM
2011Team

Wageningen University
WageningenThe Netherlands

Systems and Synthetic Biology
Wageningen University
The NetherlandsWageningen

A Multi-Platform Flow Device for Microbial (Co-) Cultivation and Microscopic Analysis
Published May 14, 2012Received November 24, 2011; Accepted April 11, 2012;Editor: Niyaz Ahmed, University of Hyderabad, India
Novel microbial cultivation platforms are of increasing interest to researchers in academia and industry. The development of materials with specialized chemical and geometric properties has opened up new possibilities in the study of previously unculturable microorganisms and has facilitated the design of elegant, high-throughput experimental set-ups. Within the context of the international Genetically Engineered Machine (iGEM) competition, we set out to design, manufacture, and implement a flow device that can accommodate multiple growth platforms, that is, a silicon nitride based microsieve and a porous aluminium oxide based microdish. It provides control over (co-)culturing conditions similar to a chemostat, while allowing organisms to be observed microscopically. The device was designed to be affordable, reusable, and above all, versatile. To test its functionality and general utility, we performed multiple experiments with Escherichia coli cells harboring synthetic gene circuits and were able to quantitatively study emerging expression dynamics in real-time via fluorescence microscopy. Furthermore, we demonstrated that the device provides a unique environment for the cultivation of nematodes, suggesting that the device could also prove useful in microscopy studies of multicellular microorganisms. Citation: Hesselman MC, Odoni DI, Ryback BM, de Groot S, van Heck RGA, et al. (2012) A Multi-Platform Flow Device for Microbial (Co-) Cultivation and Microscopic Analysis. PLoS ONE 7(5): e36982.

Introduction

In recent years there have been numerous attempts to develop novel platforms for growing previously uncultured microbes, and to expand the scope and precision of the study of bacterial model organisms. While the vast majority of bacterial and archaeal species remain uncultivable, recent studies have shown that an increasing number of environmental isolates can be grown in artificial environments that mimic the physical and chemical parameters of the organisms' natural habitat [1,2,3]. Conversely, quantitative, systems-oriented investigations of highly engineered model organisms containing synthetic regulatory networks benefit from experimental set-ups which allow precise control to be exerted over these same parameters, while physiological responses are monitored in real time without disrupting the cells [4,5,6]. These platforms range from simple do-it-yourself approaches to more high-tech methods [7,8,9]. While some of these approaches, which include microfluidic chips [10], porous metallic membranes [2], and gel microdroplets [11], among others, vary greatly in terms of scale, material, and level of sophistication, they share a number of common characteristics. In all cases the microorganisms are physically trapped, and thereby constrained in terms of mobility and population size. Nutrients are either provided through direct channels, or via diffusion across the trapping barrier. Optical transparency of the materials is also common, as it facilitates microscopic observations. Elegant microfluidic cultivation devices may not only reduce costs, but can also increase the accuracy and precision of measurements, enabling new experimental approaches and testing novel hypotheses [12]. Still, microfluidic devices frequently require sophisticated and expensive peripheral equipment, potentially complicating the simplest of analyses. Furthermore, microfluidic devices are vulnerable to fouling and contamination when used to deliver culture medium, which reduces the robustness of such methods and their usable lifetime.

Within the context of iGEM, the international Genetically Engineered Machine competition [13], a team of undergraduate students from Wageningen University re-designed a genetic circuit for synchronized oscillations, inspired by previous studies in Escherichia coli [4,6]. To study the dynamics of synchronized oscillatory gene expression, a simple -and by all means affordable-bacterial cultivation platform was required, in which quantitative fluorescence measurements on small bacterial populations could be performed. Previous work on synchronized bacterial oscillators has demonstrated the importance of providing growth conditions in which high-density, spatially constrained bacterial populations can be nutritionally sustained over prolonged periods of time [4]. Attempts to observe synchronized oscillatory behavior using conventional experimental set-ups, such as microtiter plates, proved irreproducible. In order to provide the necessary conditions, we envisioned a simple growth chamber in which small microbial populations could be immobilized and which could be operated in conjunction with a fluorescence microscope taking measurements in real time, with medium flow supplied by a standard syringe pump.

To this end, we designed, manufactured and tested a simple and reusable plastic flow chamber with a custom socket that can  accommodate two different cultivation platforms. One of the cultivation platforms in this flow chamber is a silicon nitride microsieve that is available with a range of different pore sizes, which has its origins in microfiltration applications (http://www. aquamarijn.nl/). The second platform is a microbial culture chip (microdish) made of porous aluminium oxide (PAO) (http://www. microdish.nl/) [2]. The flow device enabled us to monitor the activity of fluorescent reporter genes from microbes under the microscope, while providing control over the supply of growth media without mechanically disrupting the cells.

Materials and Methods

Design and construction of the multi-platform flow device

The device is made from polymethylmethacrylate (perspex), a transparent, durable, autoclavable, and inexpensive material ( Figure 1). The in-and outlets are threaded, which allows plugs and tubing to be screwed in securely. The material is resistant to ethanol and chlorine-based cleaning solutions, and can be cleaned with these to sterilize the device between experiments (http:// www.gehr.de/dyndata/PMMA_engl.pdf). The device is not as susceptible to biofouling as microfluidic chips due to the internal dimensions that allow cleaning solutions to remove any biological residue. We did not observe any residual contamination after repeated experiments (data not shown). The distance between the socket and the top of the chamber is ,1 mm. This proximity allows for a magnification with a 20x objective for a total magnification of 200x using an Olympus Microscope BX41, which allows the detection of fluorescence emitted by small agglomerations of cells. Detection of single bacterial cells may be also possible using objectives with higher magnification as long as the minimum focal depth is no less than 1 mm, such as 40x and 60x water-immersion objectives of the Olympus LUMPLFLN-W series. The dimensions of the chamber were chosen such that both growing platforms could be accommodated. The resulting total volume of the main chamber is ,0.5 ml. The device can be sealed with a thin glass slide using Bison mastic silicon kit, which is removable and enables the reuse of the device after an experiment. The flow device was used with a ProSense syringe pump NE1000X2 for the flow of media or buffers which is connected to the device via transparent silicon tubing. The flow device was designed using Google SketchUp (http://sketchup.google.com), and manufactured with a standard workbench computer controlled drill in the fine mechanical workshop of Wageningen University. The sketch files of the device are available for users to customize and manufacture their own flow device (File S1). The cost of a single, reusable device, excluding a microsieve or microdish, can be approximated to be ,150 Euros, the bulk of which can be attributed to manual labor costs.

Microsieve cultivation

Microsieves are inorganic membranes made of a thin layer of silicon-rich silicon nitride [14]. The microsieve comes in a wide range of variants with respect to different well-defined pore sizes (0.2 mm-0.45 mm), thicknesses (0.1-1 mm) and different levels of porosity. The silicon nitride has high thermal stability, chemical inertness and mechanical strength. Together this allows high flux performance with low trans-membrane pressure and size-selectivity [15,16,17,18].

To retain fluorescent Escherichia coli cells on the microsieve (pore size 0.45 mm), the cells were inoculated through the top inlet channel while applying negative pressure below the sieve to partly remove the liquid and retain the bacteria on the sieve. This was done manually with the use of a syringe attached via plastic tubing to one of the lower channels.

Microdish cultivation

The material (PAO) used for the microdish is a broadly applicable and modifiable matrix enabling an increased cultivation efficiency, for example of pathogens [19,20], and is available with and without circular microscopic subdivisions into micron scale wells, which can serve as growth chambers. The microdish comes in a wide range of variants with respect to the presence and absence of subcompartments, subcompartment dimensions, chemical modifications of the PAO, but also with respect to the thickness and pore size and porosity of the cultivation chip itself. Together this allows for a range of dynamics with respect to the diffusion rate of small molecules and the ability to passage or exclude macromolecules.

A compartmentalized microdish cultivation chip, containing 40 mm deep wells with a diameter of 180 mm, was tested in the flow device. An overnight-grown E. coli culture containing a synthetic gene construct capable of producing synchronized oscillatory gene expression was resuspended in phosphate buffered saline (PBS). The resuspended culture was used to inoculate the device through the top inlet channel. LB medium was supplied via the lower inlet channel, thus restricting the growth of bacteria to the wells where nutrients could be obtained through the porous material at the base. Several methods that allow wells to be individually inoculated have been described [21,22,23]. However, these specialized inoculation techniques were not necessary for the experiments performed during the validation of our device. Using an Olympus BX41 microscope equipped with a GFP filter, variations in fluorescent light emission were measured. The light source was a HBO 103W/2 Osram mercury lamp. Images were captured by a CCD camera with an exposure time of 200 ms. Cells were illuminated for 2 seconds for every measurement. The shutter was robotically controlled to ensure consistent illumination at each time point. Data analysis and processing were done with the software ImageJ 1.45 (http://rsbweb.nih.gov/ij/index.html).

Additionally, experiments to test the applicability of co-cultivation in the microsieve and microdish were performed. For this, cultures of inducer and inducible cells were grown separately overnight. The inducible cells were inoculated in the top compartment of the flow chamber containing a microsieve, followed shortly thereafter   by the inoculation of inducer cells in the lower compartment. For the same experiment in the microdish, the inducible cells were resuspended in PBS before inoculating via the top inlet channel. LB media was supplied via the lower inlet channel to enable the cells to grow in the wells. After letting the inducible cells grow in the dish for one night, the inducer cells were injected into the lower compartment. In both experimental set-ups, GFP measurements of the top cells were taken in short intervals (5-10 minutes) directly after inoculation of the inducer cells.

Organisms and constructs

The bacteria used for the experiments were E. coli TOP10 cells (Invitrogen) with a pSB1A2 plasmid backbone encoding ampicillin resistance (http://partsregistry.org/Part:pSB1A2) and containing different BioBrick parts as they can be found in the Registry of Standard Biological Parts (http://partsregistry.org). BioBrick standard biological parts are DNA sequences that adhere to defined standards to facilitate the modular assembly of complex genetic constructs from simple subcomponents. The principle feature of BioBrick parts is the presence of unique restriction sites flanking the functional DNA elements [24]. The parts used are depicted in table 1.

The liquid cultures were made from single colonies from cell cultures grown on LB agar plates with ampicillin (50 mg/mL). These were grown over night at 37uC in 10 ml of LB medium containing ampicillin. The cultures were either directly inoculated in the device or centrifuged and resuspended in PBS, depending on the experimental set-up.

To assess whether the flow device could be used for other organisms, an engineered Caenorhabditis elegans strain PD4792 expressing green fluorescent protein (GFP) exclusively in the oesophagus (www.wormbase.org) was injected into the top chamber. The fluorescence allows for easier detection of the nematodes, but the animals are also visible with white light from above. In an additional experiment, the wells were first seeded with green fluorescent protein producing E. coli, serving as a food source for the animals. The nematodes were placed in the upper compartment the next day and the liquid over the wells was removed. Nematodes that remained trapped in the wells were left in the dish for another night.

Results and Discussion

Retention of bacteria on a microsieve

The device containing a microsieve was inoculated with fluorescent E. coli cells. The bacteria were retained predominantly on the diagonal permeable areas, where small clusters of cells could be discerned. Even after applying a gentle flow over the retained cells, potentially allowing exposure of the cells to other compounds in the context of biosensing, the cells remained trapped on the sieve (Figure 2).

Synchronized oscillatory gene expression in a microdish

Certain intercellular signalling networks consisting of regulatory feedback loops can result in synchronized oscillatory gene expression. However, the emergence of such phenomena is generally not very robust, and often contingent on specific environmental parameters. Temporal variations in GFP expression produced by E. coli microcolonies harboring such a signalling construct were detected using fluorescence microscopy. The averaged and normalized fluorescent emission intensities plotted against time clearly depict synchronized oscillatory behavior with a period of approximately 1 h (Figure 3). Since the aim of this experiment was to detect relative changes in fluorescence intensities over time in order to confirm the oscillatory properties of the construct rather than measurement of absolute GFP expression values, it was not necessary to normalize the measured intensities against the cell density.

Co-cultivation experiments

In nature, most microorganisms grow in co-culture with other microbes, and interactions mediated by diffusible signalling compounds are critical in this process and often essential for growth [1,3]. Relatively simple co-cultivation systems based around porous membranes have led to major improvements in the cultivation of otherwise intractable species. In the laboratory, co-cultivation experiments are comparatively rare and usually confined to defined pairs of microorganisms.

The separation of the flow chamber into two compartments by either of the two cultivation platforms allows the co-cultivation of different organisms separated by the permeable platform, thereby enabling an exchange of small molecules, for example in codependent cross-feeding experiments [25]. Alternatively, since PAO also allows for eukaryotic cell culturing [26], this flow device could allow for experiments on growth promotion, infectivity or toxicity.

To test the applicability of the set-up for co-cultivation, we assessed whether a signalling molecule produced by bacteria in the lower compartment would induce gene expression of bacteria in the top compartment. The signalling molecule produced by the bacteria in the lower compartment (inducer cells) should easily diffuse through the permeable platform and be bound by a receptor present in the bacteria in the top compartment (inducible cells). The resulting complex in turn activates gene expression of a detectable reporter, in this case GFP. The experiment was carried out in flow devices equipped with either a microsieve or a microdish.

Initial measurements of the inducible cells in the top compartment showed hardly detectable amounts of basally expressed GFP. After addition of the inducer culture in the lower compartment, a rapid increase of GFP expression was observed, both in the microsieve ( Figure 4) and the microdish ( Figure 5). The observed increase was too rapid to be the result of cell growth alone. This can be concluded from the observation that cells constitutively expressing GFP do not display increases in fluorescence of a comparable rate and magnitude (data not shown). In order to validate the barrier function of the two platforms, the suspension of inducer cells was spiked with E. coli cells expressing RFP. Additionally to the GFP measurements, the top compartment was monitored for presence of RFP. Leakage was not observed, indicating that the edges of the respective growing platforms were sealed properly.

The system presented here would allow the gap between nature and the laboratory to be bridged, with thousands of different microorganisms (in discrete microwells) to be co-cultivated with a common partner organism (beneath the microdish), a major increase in combinations that may be further improvable with higher well densities to allow genuinely high throughput screenings for microbial interactions.

Using the flow device with nematodes

The device could be useful for the cultivation of organisms other than bacteria, for example for Caenorhabditis elegans, a nematode approximately 1 mm in length and 80 mm in width. The nematodes inoculated in the upper compartment of a flow device containing a microdish were easily discernible (Figure 6), and capable of moving over the wells in the dish when the top chamber of the device was filled with liquid. Cultivation experiments on PAO indicated that the full developmental cycle, from eggs to adult, could occur on this material (data not shown). The pictures taken one day after inoculation revealed that the wells containing worms were almost completely devoid of the bacteria, while wells without the animals were still filled with the fluorescent E. coli cells ( Figure 6). This highlights a potential use of the flow device with multicellular organisms. Approaches to the analysis of nematodes using microfluidics have used relatively complicated platforms requiring multistep fabrication techniques. Furthermore, these approaches have been limited in throughput, resulting from the need to supply individual chambers with input and output channels [10,27,28]. Given the importance of nematodes in drug screening, e.g. drugs targets against the central nervous system, robust high-throughput methods are desirable and the method presented here provides a route to one, particularly given the potential to add drugs either from beneath the culture chip or by non-contact printing into individual wells or by co-culture with bacterial strains expressing siRNAs.

Conclusions

The recent development of novel microbial cultivation platforms has provided researchers with a wide variety of options for isolating and studying microorganisms. It is important to be aware of the advantages and drawbacks of the available platforms. Factors such as population size, geometry, control over medium composition, parallelization and multiplexing capacity need to be weighed against the cost of, and the expertise and peripheral equipment required to operate a given platform. The most basic implementation of what can be considered a novel cultivation platform is a simple matrix, which combines cell adhesion with nutrient diffusion. Such set-ups are inexpensive, and easy to implement as they do not require sophisticated peripheral equipment. On the other end of the spectrum, there are nanoscale microbioreactor chambers, which allow individual cells to be subjected to precisely controlled chemical environments [7]. The microbial flow device described in this paper is somewhere in between. It is well suited for fluorescence microscopy studies of small, high-density cellular populations over extended time periods, as it provides control over the composition of liquid media without disrupting the cells. We have demonstrated its utility in the study of intercellular signalling, dynamic gene regulatory networks, and nematode cultivation. Other potential applications of this device include microbial biosensing, toxicity measurements and chemotaxis studies. Furthermore, since the platforms used in this device allow for the cultivation of eukaryotic cells, it could be of use in the real-time monitoring of co-cultivation and infection experiments.

We believe that this device has the potential to make advanced cultivation materials accessible to a wider range of users. While we do not anticipate that this platform will replace existing technologies, we hope that our design, and modifications thereof, will prove useful where versatility, cost, and simplicity of use need to be emphasized over experimental precision, such as in the DIY biology community and in iGEM projects.

Supporting Information

Figure 1 .
1Schematic representation of the flow device. A) Schematic representation of the flow device, with the dimensions in mm. Depicted in red and blue are the in-and outflow channels of the top compartment (light green). The respective in-and outflow channels of the lower compartment (yellow) are given in purple and dark green. B) Electron microscopy image of a microsieve. C) Electron microscopy image of a microdish. See File S1 for more views of the device. doi:10.1371/journal.pone.0036982.g001

Figure 2 .
2Cell retention on the microsieve. Retained clusters of fluorescent E. coli cells on the sieve (100x magnification). The diagonal light grey bands are the permeable areas of the microsieve that contain the pores. doi:10.1371/journal.pone.0036982.g002

Figure 3 .
3Oscillating GFP expression observed in microdish wells. Fluorescent E. coli cells in the wells of the microdish showing variations in signal strength over time. The graph depicts variations plotted with the image analysis and processing tool ImageJ. The x-axis represents time, and the y-axis represents fluorescence (in arbitrary units and with a variance of maximally 0.01 for the normalised data of 5 wells). Below the graph are microscopic images of fluorescent bacteria in the cultivation chip wells at different intervals using identical illumination conditions and CCD camera exposure times. The time points at which the images were taken are indicated with an asterisk. doi:10.1371/journal.pone.0036982.g003

Figure 4 .
4Co-cultivation of cells separated by a microsieve. Increase of GFP expression of inducible cells on the sieve after inoculation of inducer cells below. Graph plotted with the image analysis and processing tool ImageJ. The x-axis corresponds to time and the y-axis shows the detected GFP signal (in arbitrary units). Below: a number of representative images of the microsieve. The time points at which the images were taken are indicated with an asterisk. doi:10.1371/journal.pone.0036982.g004

Figure 5 .
5Co-cultivation of cells in the flow device. Expression of GFP increases over time in cells growing in microdish wells after adding inducer (Acyl homoserine lactone (AHL) producing) and RFP expressing cells to the bottom compartment of the flow device. The intensity of the light emitted from five wells was quantified using imageJ and normalized against the background. No RFP was detected in the top chamber, indicating that the inducer-cells added to the bottom chamber did not come in contact with the top, and GFP expression was induced by diffusion of the inducer (AHL) through the microdish. doi:10.1371/journal.pone.0036982.g005

Figure 6 .
6Fluorescent nematodes observed in the flow device. A) Nematodes floating over the wells while the chamber is filled with liquid. The fluorescent oesophagus in the front side of the nematode is clearly visible. B) Nematode trapped in a well filled with fluorescent E. coli cells after removing the liquid from the chamber. C) Next day: A nematode after consuming all fluorescent bacteria from the well, resulting in observable fluorescence in the nematode intestine. doi:10.1371/journal.pone.0036982.g006

File S1 .
S1Schematic representations of the flow device, with the dimensions in mm. Depicted in blue and red are the in-and outflow channels of the top compartment (light green). The microdish socket is shown in grey, and the microsieve socket is shown in dark purple. The respective in-and outflow channels of the lower compartment (yellow) are given in light purple and dark green. A) Side view of the flow device without external framework. B) Top view of the flow device without external framework. C) Zoomed in top view of the flow device including microsieve and microdish socket dimensions. D) Top view of top compartment of the flow device including top compartment dimensions. E) Bottom side view flow device without external framework. (DOCX)

Table 1 .
1Overview of the BioBrick parts used.Experimental set-up
BioBrick part
Reference

Variations in signal strength over time
BBa_K546546
http://partsregistry.org/Part:BBa_K546546

Co-cultivation:

Inducible cells
BBa_K546002
http://partsregistry.org/Part:BBa_K546002

Inducer cells*
BBa_K546000
http://partsregistry.org/Part:BBa_K546000

BBa_I721007
http://partsregistry.org/Part:BBa_I721007

*co-cultivated with RFP containing BioBrick part as control for leakage.
doi:10.1371/journal.pone.0036982.t001

PLoS ONE | www.plosone.org
May 2012 | Volume 7 | Issue 5 | e36982
AcknowledgmentsWe gratefully acknowledge the help of Mariana Matus Garcia, Shi Wang, Jacob Baggerman (Aquamarijn), Hans de Rooij and Joost Riksen, all employed or studying at Wageningen University.Author Contributions
Isolating ''uncultivable'' microorganisms in pure culture in a simulated natural environment. T Kaeberlein, K Lewis, S S Epstein, Science. 296Kaeberlein T, Lewis K, Epstein SS (2002) Isolating ''uncultivable'' microor- ganisms in pure culture in a simulated natural environment. Science 296: 1127-1129.

The micro-Petri dish, a million-well growth chip for the culture and highthroughput screening of microorganisms. C J Ingham, A Sprenkels, J Bomer, D Molenaar, A Van Den Berg, Proc Natl Acad Sci U S A. 104Ingham CJ, Sprenkels A, Bomer J, Molenaar D, van den Berg A, et al. (2007) The micro-Petri dish, a million-well growth chip for the culture and high- throughput screening of microorganisms. Proc Natl Acad Sci U S A 104: 18217-18222.

Hollow-fiber membrane chamber as a device for in situ environmental cultivation. Y Aoi, T Kinoshita, T Hata, H Ohta, H Obokata, Appl Environ Microbiol. 75Aoi Y, Kinoshita T, Hata T, Ohta H, Obokata H, et al. (2009) Hollow-fiber membrane chamber as a device for in situ environmental cultivation. Appl Environ Microbiol 75: 3826-3833.

A synchronized quorum of genetic clocks. T Danino, O Mondragon-Palomino, L Tsimring, J Hasty, Nature. 463Danino T, Mondragon-Palomino O, Tsimring L, Hasty J (2010) A synchronized quorum of genetic clocks. Nature 463: 326-330.

Microfluidic technologies for synthetic biology. P Vinuselvi, S Park, M Kim, J M Park, T Kim, Int J Mol Sci. 12Vinuselvi P, Park S, Kim M, Park JM, Kim T, et al. (2011) Microfluidic technologies for synthetic biology. Int J Mol Sci 12: 3576-3593.

Entrainment of a population of synthetic genetic oscillators. O Mondragon-Palomino, T Danino, J Selimkhanov, L Tsimring, J Hasty, Science. 333Mondragon-Palomino O, Danino T, Selimkhanov J, Tsimring L, Hasty J (2011) Entrainment of a population of synthetic genetic oscillators. Science 333: 1315-1319.

Long-term monitoring of bacteria undergoing programmed population control in a microchemostat. F K Balagadde, L You, C L Hansen, F H Arnold, S R Quake, Science. 309Balagadde FK, You L, Hansen CL, Arnold FH, Quake SR (2005) Long-term monitoring of bacteria undergoing programmed population control in a microchemostat. Science 309: 137-140.

Selfloading lithographically structured microcontainers: 3D patterned, mobile microwells. T G Leong, C L Randall, B R Benson, A M Zarafshar, D H Gracias, Lab Chip. 8Leong TG, Randall CL, Benson BR, Zarafshar AM, Gracias DH (2008) Self- loading lithographically structured microcontainers: 3D patterned, mobile microwells. Lab Chip 8: 1621-1624.

A microwell array device with integrated microfluidic components for enhanced single-cell analysis. S Lindstrom, K Mori, T Ohashi, H Andersson-Svahn, Electrophoresis. 30Lindstrom S, Mori K, Ohashi T, Andersson-Svahn H (2009) A microwell array device with integrated microfluidic components for enhanced single-cell analysis. Electrophoresis 30: 4166-4171.

Microfluidic chamber arrays for whole-organism behavior-based chemical screening. K Chung, M Zhan, J Srinivasan, P W Sternberg, E Gong, Lab Chip. 11Chung K, Zhan M, Srinivasan J, Sternberg PW, Gong E, et al. (2011) Microfluidic chamber arrays for whole-organism behavior-based chemical screening. Lab Chip 11: 3689-3697.

Cultivating the uncultured. K Zengler, G Toledo, M Rappe, J Elkins, E J Mathur, Proc Natl Acad Sci U S A. 99Zengler K, Toledo G, Rappe M, Elkins J, Mathur EJ, et al. (2002) Cultivating the uncultured. Proc Natl Acad Sci U S A 99: 15681-15686.

Acceleration of Emergence of Bacterial Antibiotic Resistance in Connected Microenvironments. Q Zhang, G Lambert, D Liao, H Kim, K Robin, Science. 333Zhang Q, Lambert G, Liao D, Kim H, Robin K, et al. (2011) Acceleration of Emergence of Bacterial Antibiotic Resistance in Connected Microenvironments. Science 333: 1764-1767.

Building outside of the box: iGEM and the BioBricks Foundation. C D Smolke, Nat Biotechnol. 27Smolke CD (2009) Building outside of the box: iGEM and the BioBricks Foundation. Nat Biotechnol 27: 1099-1102.

Nanosieves with microsystem technology for microfiltration applications. Cjm Van Rijn, G J Veldhuis, S Kuiper, Nanotechnology. 9van Rijn CJM, Veldhuis GJ, Kuiper S (1998) Nanosieves with microsystem technology for microfiltration applications. Nanotechnology 9: 343-345.

Ceramic microsieves: influence of perforation shape and distribution on flow resistance and membrane strength. S Kuiper, R Brink, W Nijdam, Gjm Krijnen, M C Elwenspoek, Journal of Membrane Science. 196Kuiper S, Brink R, Nijdam W, Krijnen GJM, Elwenspoek MC (2002) Ceramic microsieves: influence of perforation shape and distribution on flow resistance and membrane strength. Journal of Membrane Science 196: 149-157.

The role of wetting on the water flux performance of microsieve membranes. M Girones, A Borneman, Rgh Lammertink, M Wessling, Journal of Membrane Science. 259Girones M, Borneman A, Lammertink RGH, Wessling M (2005) The role of wetting on the water flux performance of microsieve membranes. Journal of Membrane Science 259: 55-64.

Evaluation of microsieve membrane design. G Brans, J Kromkamp, N Pek, J Gielen, J Heck, Journal of Membrane Science. 278Brans G, Kromkamp J, Pek N, Gielen J, Heck J, et al. (2006) Evaluation of microsieve membrane design. Journal of Membrane Science 278: 344-348.

Development and applications of very high flux microfilttration membranes. S Kuiper, Cjm Van Rijn, W Nijdam, M C Elwenspoek, Journal of Membrane Science. 150Kuiper S, van Rijn CJM, Nijdam W, Elwenspoek MC (1998) Development and applications of very high flux microfilttration membranes. Journal of Membrane Science 150: 1-8.

Rapid drug susceptibility testing of mycobacteria by culture on a highly porous ceramic support. C J Ingham, A B Ayad, K Nolsen, B Mulder, Int J Tuberc Lung Dis. 12Ingham CJ, Ayad AB, Nolsen K, Mulder B (2008) Rapid drug susceptibility testing of mycobacteria by culture on a highly porous ceramic support. Int J Tuberc Lung Dis 12: 645-650.

Rapid antibiotic sensitivity testing and trimethoprim-mediated filamentation of clinical isolates of the Enterobacteriaceae assayed on a novel porous culture support. C J Ingham, M Van Den Ende, P C Wever, P M Schneeberger, J Med Microbiol. 55Ingham CJ, van den Ende M, Wever PC, Schneeberger PM (2006) Rapid antibiotic sensitivity testing and trimethoprim-mediated filamentation of clinical isolates of the Enterobacteriaceae assayed on a novel porous culture support. J Med Microbiol 55: 1511-1519.

A printed nanolitre-scale bacterial sensor array. S Melamed, L Ceriotti, W Weigel, F Rossi, P Colpo, Lab Chip. 11Melamed S, Ceriotti L, Weigel W, Rossi F, Colpo P, et al. (2011) A printed nanolitre-scale bacterial sensor array. Lab Chip 11: 139-146.

Microcontact printing of living bacteria arrays with cellular resolution. L Xu, L Robert, Q Ouyang, F Taddei, Y Chen, Nano Lett. 7Xu L, Robert L, Ouyang Q, Taddei F, Chen Y, et al. (2007) Microcontact printing of living bacteria arrays with cellular resolution. Nano Lett 7: 2068-2072.

Highresolution microcontact printing and transfer of massive arrays of microorganisms on planar and compartmentalized nanoporous aluminium oxide. C Ingham, J Bomer, A Sprenkels, A Van Den Berg, W De Vos, Lab Chip. 10Ingham C, Bomer J, Sprenkels A, van den Berg A, de Vos W, et al. (2010) High- resolution microcontact printing and transfer of massive arrays of microorgan- isms on planar and compartmentalized nanoporous aluminium oxide. Lab Chip 10: 1410-1416.

Engineering BioBrick vectors from BioBrick parts. R P Shetty, D Endy, T F KnightJr, J Biol Eng. 25Shetty RP, Endy D, Knight TF Jr. (2008) Engineering BioBrick vectors from BioBrick parts. J Biol Eng 2: 5.

Novel cooperation experimentally evolved between species. W Harcombe, Evolution. 64Harcombe W (2010) Novel cooperation experimentally evolved between species. Evolution 64: 2166-2172.

Where bio meets nano: The many uses for nanoporous aluminum oxide in biotechnology. C J Ingham, Ter Maat, J De Vos, W M , 10.1016/j.biotechadv.2011.08.005Biotechnol Adv. Ingham CJ, Ter Maat J, de Vos WM (2011) Where bio meets nano: The many uses for nanoporous aluminum oxide in biotechnology. Biotechnol Adv doi:10.1016/j.biotechadv.2011.08.005.

A microfluidic platform for high-sensitivity, real-time drug screening on C. elegans and parasitic nematodes. J A Carr, A Parashar, R Gibson, A P Robertson, R J Martin, Lab Chip. 11Carr JA, Parashar A, Gibson R, Robertson AP, Martin RJ, et al. (2011) A microfluidic platform for high-sensitivity, real-time drug screening on C. elegans and parasitic nematodes. Lab Chip 11: 2385-2396.

Long-term high-resolution imaging and culture of C. elegans in chip-gel hybrid microfluidic device for developmental studies. J Krajniak, H Lu, Lab Chip. 10Krajniak J, Lu H (2010) Long-term high-resolution imaging and culture of C. elegans in chip-gel hybrid microfluidic device for developmental studies. Lab Chip 10: 1862-1868.


Clinical consequences of switching from olanzapine to risperidone and vice versa in outpatients with schizophrenia: 36-month results from the worldwide schizophrenia outpatients health outcomes (W-SOHO) study

Jihyung Hong
Diego Novick
Roberto Brugnoli
Jamie Karagianis
Martin Dossenbach
Josep Maria Haro
Clinical consequences of switching from olanzapine to risperidone and vice versa in outpatients with schizophrenia: 36-month results from the worldwide schizophrenia outpatients health outcomes (W-SOHO) study
R E S E A R C H A R T I C L E Open Access
Background: With many atypical antipsychotics now available in the market, it has become a common clinical practice to switch between atypical agents as a means of achieving the best clinical outcomes. This study aimed to examine the impact of switching from olanzapine to risperidone and vice versa on clinical status and tolerability outcomes in outpatients with schizophrenia in a naturalistic setting. Methods: W-SOHO was a 3-year observational study that involved over 17,000 outpatients with schizophrenia from 37 countries worldwide. The present post hoc study focused on the subgroup of patients who started taking olanzapine at baseline and subsequently made the first switch to risperidone (n=162) and vice versa (n=136). Clinical status was assessed at the visit when the first switch was made (i.e. before switching) and after switching. Logistic regression models examined the impact of medication switch on tolerability outcomes, and linear regression models assessed the association between medication switch and change in the Clinical Global Impression-Schizophrenia (CGI-SCH) overall score or change in weight. In addition, Kaplan-Meier survival curves and Cox-proportional hazards models were used to analyze the time to medication switch as well as time to relapse (symptom worsening as assessed by the CGI-SCH scale or hospitalization). Results: 48% and 39% of patients switching to olanzapine and risperidone, respectively, remained on the medication without further switches (p=0.019). Patients switching to olanzapine were significantly less likely to experience relapse (hazard ratio: 3.43, 95% CI: 1.43, 8.26), extrapyramidal symptoms (odds ratio [OR]: 4.02, 95% CI: 1.49, 10.89) and amenorrhea/galactorrhea (OR: 8.99, 95% CI: 2.30, 35.13). No significant difference in weight change was, however, found between the two groups. While the CGI-SCH overall score improved in both groups after switching, there was a significantly greater change in those who switched to olanzapine (difference of 0.29 points, p=0.013). Conclusion: Our study showed that patients who switched from risperidone to olanzapine were likely to experience a more favorable treatment course than those who switched from olanzapine to risperidone. Given the nature of observational study design and small sample size, additional studies are warranted.

Background

With many atypical antipsychotics now available in the market, it has become a common clinical practice to switch between atypical agents as a means of achieving the best clinical outcomes [1]. It is, therefore, crucial for clinicians and decision makers to understand the possible benefits and risks associated with switching between atypical agents. Because of the potential implications on prevailing standards of clinical practice, the consequences of switching between olanzapine and risperidone are of particular interest as they are the two most commonly prescribed antipsychotics [2,3].

Some prior research has shown the clinical utility of switching from olanzapine to risperidone or vice versa. Ganguli et al. (2008) conducted an open-label trial to evaluate the efficacy and safety of risperidone in patients with schizophrenia who had been non-responsive or intolerant to olanzapine. The results of the study showed that switching from olanzapine to risperidone, regardless of whether the switching was abrupt or gradual, was associated with significant symptom improvement, especially in positive and anxiety symptoms, and was generally well tolerated. Clinical benefits of switching to risperidone after insufficient response to olanzapine have also been reported by Takahashi et al. (2006b) who focused on patients with first-episode schizophrenia. Conversely, in another open-label trial involving the same type of patients, these authors found that nonresponders to risperidone treatment benefited from a switch to olanzapine [4]. The benefit of switching from risperidone to olanzapine was also reported in other open-label trials involving a broader spectrum of patients with schizophrenia [5,6]. While the experimental data from these open-label trials confirmed the benefits of switching between risperidone and olanzapine, patient behavior and outcomes are likely to differ in actual clinical settings. Moreover, except for one costing study [1], there have been no direct comparisons between the switch from olanzapine to risperidone and the switch from risperidone to olanzapine. Using a claims database, Zhao et al. (2004) reported that patients who switched from risperidone to olanzapine had a decrease in total medical costs despite an increase in medicationrelated costs, whereas those switching from olanzapine to risperidone had no significant change in total health care costs despite decreased medication-related costs. Most patients with schizophrenia who change medication do so because they have severe disease that is not fully responsive to the existing medication, so switching to another medication is likely to show improvements. Thus, the relative benefits of two medications can only be fully understood by comparing the changes achieved when switching from one medication to the other and vice versa.

To better understand the clinical implications of switching between risperidone and olanzapine in the 'real world' setting, the present study conducted a post hoc analysis using data accrued from the large naturalistic 3-year W-SOHO (Worldwide-Schizophrenia Outpatient Health Outcomes) study. The specific objectives of the present analysis were two-fold: (1) to compare clinical outcomes before and after switching from oral risperidone to oral olanzapine as well as switching from oral olanzapine to oral risperidone, respectively, and (2) to compare the post-switch clinical outcomes between the two groups.

Methods

Study design and patient sample

The Schizophrenia Outpatient Health Outcomes (SOHO) study was a 3-year, international, prospective, observational study primarily designed to assess the comparative costs and outcomes associated with antipsychotic use in outpatients initiating or changing antipsychotic medication for schizophrenia (with an emphasis on olanzapine compared with other antipsychotics). SOHO was conducted in 10 Western European countries (EU-SOHO) [7,8], and in 27 countries across 4 continents as the Intercontinental SOHO (IC-SOHO) [9]. Data from all 37 participating countries were pooled to produce the Worldwide-SOHO (W-SOHO) dataset. A total of 17,384 patients were included in W-SOHO, and the details of the study are available elsewhere [10]. The study was carried out in accordance with the ethical standards of responsible local committees and regulations of the participating countries [7]. It was approved in all countries at the site, regional or national level, depending on the countries' regulations and participating sites in each country. Patient consent followed country regulations. All patients gave at least oral informed consent and written informed consent was obtained in Denmark, Italy, Portugal, Spain, Ireland, and the UK.

Participating psychiatrists offered enrolment to adult patients (at least 18 years of age) initiating or changing antipsychotic medication for the treatment of schizophrenia, who presented within the normal course of care in the outpatient setting or in the hospital when admission was planned for the initiation or change of antipsychotic medication and discharge was planned within 2 weeks, and who were not participating in another interventional study. The diagnosis of schizophrenia was made by the participating psychiatrists using standard diagnostic criteria [Diagnostic and Statistical Manual of Mental Disorders 4 th ed [11] or International Classification of Diseases 10 th ed [12]]. Patient enrolment began in September 2000 for EU-SOHO and in November 2000 for IC-SOHO; the last patient was enrolled in December 2001. The enrolment period was intentionally long to avoid interfering with standard medical practice and no minimum number of patients was required per participating psychiatrist.

As the initial objective of SOHO was to compare the outcomes of patients starting olanzapine with other antipsychotics, the study was designed to provide two patient cohorts of approximately equal size: (1) patients starting olanzapine, and (2) patients starting any other antipsychotic. This deliberate over-sampling of olanzapine patients was done to facilitate comparisons between the two groups, in accordance with the primary objective. Importantly, the antipsychotic treatment prescribed to each patient was wholly based on the opinion of the treating psychiatrist; patients were asked to participate in the study after they had received their medication prescription. In addition, patients were not required to continue taking the medication initiated at baseline. Changes in medication, dosing and concomitant medication were possible at any time as determined by the treating psychiatrist.

Data collection for the study occurred at the baseline visit and at follow-up visits (i.e. 3, 6, 12, 18, 24, 30 and 36 months post-baseline) within the normal course of care. Socio-demographic data were recorded at the baseline assessment. Clinical severity was assessed at each visit using a scale based on the Clinical Global Impressions Severity Scale -Schizophrenia version (CGI-SCH) [13], which evaluates symptom severity across positive, negative, depressive and cognitive sub-domains as well as overall symptoms from 1 (normal, not ill) to 7 (among the most severely ill). Other information collected at follow-up visits included clinical status (e.g., weights (kg), alcohol/substance abuse/dependency, suicide attempts, occurrence of violent or aggressive behavior), functional status (e.g., relationships, housing conditions, work and social contacts), antipsychotic medication (drug name, formulation, dosage and reasons for medication change if applicable), concomitant medication (anticholinergics, antidepressant, anxiolytics/hypnotics and mood stabilizers), adverse events, quality of life, and health service use.

Statistical analysis

Patients with no more than one missing visit (excluding the last visit) were eligible for inclusion in the present analysis (n=11,078, 64% of the baseline sample). For patients with one missing visit, values from the previous visit were carried forward to impute the values of the missing visit. Of the 11,078 study completers, the present study focused on the subgroup of patients who started with olanzapine monotherapy and switched to risperidone monotherapy (n=162) (termed as the 'OLZ-RIS' group hereafter) or started with risperidone monotherapy and switched to olanzapine monotherapy (n=136) (termed as the 'RIS-OLZ' group hereafter) as the first medication switch during follow-up.

Kaplan-Meier survival curves of the time to (second) medication switch were plotted for each group (OLZ-RIS and RIS-OLZ). The overall difference between survival curves was compared using the log-rank test. Medication switch was defined as: (1) stopping the treatment with or without replacing it with another antipsychotic; or (2) adding a new antipsychotic to the treatment. Time to switch was also examined using a Cox proportional hazards model, adjusting for patient characteristics before switching.

The analysis focused on the following key outcomes: change in CGI-SCH overall score; extrapyramidal symptoms (EPS); loss of libido; impotence/sexual dysfunction; amenorrhea/galactorrhea; weight (kg) change; and time to relapse. Outcomes before and after switching were compared within each group using McNemar tests (for categorical variables) and paired t-tests (for numerical variables), except for time to relapse. Outcomes before switching were assessed at the visit when the first switch was made. Outcomes after switching were measured at the visit when a further switching occurred or otherwise at 36-months. Multivariate regression models (logistic or linear regressions) were also used to examine the impact of switching on these outcomes, adjusting for patient characteristics.

Time to relapse was examined only among patients who attained a CGI-SCH overall score of ≤3 (i.e. mildly ill or less) after the first switch from olanzapine to risperidone (or vice versa) but before a further switch was made or before 36 months. Starting from this new "baseline", relapse was defined as an increase of at least 2 points on the GGI-SCH overall severity score from the minimum score achieved by the patient during the follow-up assessment (till a further switch occurred; otherwise till 36-months), resulting in a rating of moderately ill or worse (score ≥4), or having had a hospitalization [14]. Of the 298 patients included, only 186 patients met this criterion (n=98 in the OLZ-RIS group and n=88 in the RIS-OLZ group). Time to relapse for the OLZ-RIS and RIS-OLZ groups was estimated using Kaplan-Meier survival curves, and also analyzed using Cox-proportional hazards model controlling for patient characteristics.

In all multivariate analyses (Cox, logistic, and linear regressions), patient group (i.e. RIS-OLZ group or OLZ-RIS group) served as the main explanatory variable while adjusting for the influence of the following key covariates: time of switch; age; gender; region; and CGI-SCH overall score before switching. Corresponding tolerability outcomes before switching were also included in the analysis of post-switch tolerability outcomes. In addition, other variables before switching were included in models, subject to their significance at p<0.05 through a backward stepwise reduction technique: time since first service contact for schizophrenia; current alcohol abuse/ dependency; current substance abuse/dependency; hostility; having a relationship; having paid employment; living independently; being socially active; and taking concomitant medications (anticholinergics, antidepressant, anxiolytics/hypnotics, and mood stabilizers).

All analyses were also repeated in a sensitivity analysis, which defined the (second) medication switch as stopping the treatment with or without replacing it with another antipsychotic. That is, in this sensitivity analysis, adding a new antipsychotic to the treatment was not considered as a switching.

Results

Of the 11,078 study completers, 6,412 patients started with either olanzapine monotherapy (n=4,736) or risperidone monotherapy (n=1,676) at baseline. Of these, a total of 2,493 patients (38.9%) switched their baseline medications over the 3-year follow-up: 1726 (36.4%) among the patients initiated on olanzapine monotherapy and 767 (45.8%) among the patients initiated on risperidone monotherapy. The present study, however, only included a subsample of patients who switched to either olanzapine monotherapy (n=136) or risperidone monotherapy (n=162) as their first medication switch during follow-up. The most common reason for discontinuation was lack of efficacy in both groups ( Table 1). The mean dose for olanzapine was 10.1 (SD: 5.3) before switching and that for risperidone was 4.7 (SD: 3.0).

The demographics and clinical characteristics of the patients before switching are reported for both groups in Table 2. The patient characteristics were similar between the two groups, except that patients in the OLZ-RIS group were significantly more likely to be alcohol dependent/abusive before switching, compared with those in the RIS-OLZ group. Figure 1 shows the Kaplan-Meier curves for the rate of (second) medication switch during follow-up for the OLZ-RIS and RIS-OLZ groups. Patients who switched to olanzapine were more likely to remain on this medication longer (47.6% and 38.9% of patients switching to olanzapine and risperidone, respectively, made no further switches, p=0.019). The Cox-regression, which adjusted for differences in patient characteristics before switching, found an increased risk of medication switch in patients who switched to risperidone, compared with those who switched to olanzapine, although this was not statistically significant at the 0.05 level (HR= 1.44; 95% CI=0.97, 2.12; p=0.070).

Patients who started treatment with either olanzapine or risperidone experienced symptom improvement on their initial medication (as assessed by the CGI-SCH overall score), and a further improvement after the medication switch (Table 2). Patients in the RIS-OLZ group achieved a mean reduction in CGI-SCH overall score from baseline of 0.71 points (SD: 1.20) and 1.46 points (SD: 1.29) before and after the medication switch, respectively. Likewise, patients in the OLZ-RIS group achieved a mean reduction from baseline of 0.75 points (SD: 1.05) and 1.15 points (SD: 1.12) before and after the medication switch, respectively.  While both groups showed clinical improvement after switching, tolerability outcomes differed between the two groups (Table 3). Patients who switched to olanzapine experienced significant improvements after switching in all tolerability outcomes except weight change, which did not differ significantly between before and after switching. Patients who switched to risperidone did not make improvements in any of the tolerability outcomes after switching and experienced a worsening in amenorrhea/galactorrhea. In addition, the Kaplan-Meier curves showed that 85.5% of patients in the RIS-OLZ group did not experience relapse during follow-up, while only 63.9% of patients in the OLZ-RIS group did not relapse during follow-up (p=0.015) among patients who attained a CGI-SCH overall score ≤3 (i.e. mildly ill or less) (Figure 2). Consistent with this, the Cox-regression model showed that patients who switched to risperidone were more likely to experience relapse during follow-up (HR=3.43; 95% CI= 1.43, 8.26; p=0.006), compared with those who switched to olanzapine. Table 4 summarizes the results of the regression analyses. Linear regression showed improvements in the CGI-SCH overall score after switching in both groups, and that those who switched to olanzapine had a further 0.29 point reduction in this score (p=0.013). Logistic regression analyses also showed more favorable outcomes in the RIS-OLZ group after switching, compared with the OLZ-RIS group: patients who switched to risperidone were more likely to have EPS (OR=4.02; 95% CI=1.49, 10.89; p=0.006) and amenorrhea/galactorrhea (OR=8.99; 95% CI=2.30, 35.13; p=0.002). There were no statistically significant differences between the two groups after switching for the other tolerability outcomes (loss of libido, impotence/sexual dysfunction and weight change).

The results of the sensitivity analysis (where adding a new antipsychotic to the treatment was not considered  Figure 1 shows the Kaplan-Meier curves for the rate of (second) medication switch during follow-up for the OLZ-RIS and RIS-OLZ groups. The difference between before and after switching was significant only in patients who switched to olanzapine (i.e., the RIS-OLZ group). c The difference between before and after switching was not significant in either group. OLZ-RIS: Patients who switched from olanzapine to risperidone. RIS-OLZ: Patients who switched from risperidone to olanzapine. EPS: Extrapyramidal Symptom.

as a medication switch) were largely consistent with these findings (results available upon request).

Discussion

To our knowledge, this is the first study that has assessed and compared clinical outcomes (effectiveness/ tolerability) of switching from olanzapine to risperidone and vice versa in the usual course of schizophrenia care. Our results showed that patients who were treated with risperidone and subsequently switched to olanzapine for clinical reasons experienced a more favorable treatment course than patients who were treated with olanzapine and subsequently switched to risperidone. Patients who switched to olanzapine experienced significant improvements in symptom control (as assessed by the CGI-SCH overall score) and in all tolerability outcomes (EPS, loss of libido, impotence/sexual dysfunction, and amenorrhea/ galactorrhea) except for weight gain. On the other hand, patients who initiated with olanzapine and switched to risperidone did not experience improvements in any of the tolerability outcomes, although they experienced significant improvements in symptom control. The differential effect between the two groups remained even when the patient characteristics before switching were taken into account in multivariate analyses. Patients who switched to olanzapine were more likely to improve in symptom control and less likely to experience relapse, EPS and amenorrhea/galactorrhea, compared with those who switched to risperidone. Consistent with previous research [5,15], patients experienced symptom improvement after medication switch in both the RIS-OLZ and OLZ-RIS groups. However, symptom improvement was greater in those who switched from risperidone to olanzapine. This finding was partially in agreement with recent results from the Clinical Antipsychotic Trials for Intervention Effectiveness study (CATIE) [16]. In phase 2T of the CATIE study (conducted in patients who primarily experienced intolerability to their previous antipsychotic), the olanzapine group achieved greater improvement than the risperidone group  Figure 2 shows the Kaplan-Meier curves for the rate of relapse during follow-up for the OLZ-RIS and RIS-OLZ groups. This analysis was confined to those patients who attained a CGI-SCH overall score of ≤3 (i.e. mildly ill or less) after the first switch from olanzapine to risperidone (or vice versa) but before a further switch was made or before 36 months (n=98 in the OLZ-RIS group and n=88 in the RIS-OLZ group). Linear regression modeled a change in CGI-SCH overall score (or weight) before and after switching. Coefficients indicate an estimated difference in the change, compared with patients switched to olanzapine. c An odds ratio of more than 1 indicates an increased likelihood of having tolerability outcomes compared with patients switched to olanzapine.

in positive symptom control as assessed by the Positive and Negative Syndrome Scale (PANSS). However, there were no differences between groups in negative and overall symptoms. Data from other head-to-head trials are available [17], but they do not suggest that one drug is clearly more effective than the other in symptom control. However, consistent with our findings, olanzapine appears to be superior to risperidone in relapse prevention, EPS and reproductive adverse events [17]. Relapse prevention is a primary goal in the long-term treatment of schizophrenia. Surprisingly, however, there is only limited information on the comparison between risperidone and olanzapine. Nevertheless, this small body of literature also reports a lower risk of relapse in patients treated with olanzapine compared with those treated with risperidone [14,18,19].

EPS are common adverse events during treatment with antipsychotics, especially for conventional agents. They are the most common reason for non-adherence with antipsychotic medication, leading to treatment failures, relapses, poorer long-term outcomes and preventable economic costs [20][21][22]. Atypical antipsychotics have a lower risk of EPS than conventional antipsychotics, and this is a considerable asset from a tolerability and safety point of view [23]. However, the risk of EPS varies among atypical antipsychotics and dose-dependent increases in EPS have been observed with risperidone [24]. Several studies have provided the evidence that patients treated with olanzapine may have a lower risk of EPS than patients treated with risperidone [25]. Previous SOHO publications also showed a lower risk of EPS with olanzapine compared with risperidone [26]. Moreover, a UK population-based study reported reduced use of antiparkinsonian drugs (treatment for EPS) in patients who switched from conventional agents to olanzapine, but not in those who switched to risperidone [20]. A reduced use of antiparkinsonian drugs was also observed in patients who switched from risperidone to olanzapine, but not in those switched from olanzapine from risperidone [1]. Lowering the risk of EPS is crucial in the management of schizophrenia, given their clinical and economic implications.

Consistent with previous studies [27,28], our study demonstrated a lower risk of amenorrhea/galactorrhea with olanzapine compared with risperidone. One possible reason for this may be the potent effect of risperidone on prolactin elevation, although plasma prolactin concentrations were not measured in the SOHO study. Prolactin elevation is a well-known adverse event of conventional antipsychotics and of some atypical antipsychotics such as risperidone [29], although treatment with olanzapine may also elevate prolactin level and the elevation may persist during chronic administration [30].

An increase in prolactin levels can cause amenorrhea, galactorrhea and other sexual disturbances [29,31]. A number of studies have shown that the risk of prolactin elevation with risperidone is similar to or greater than that of conventional antipsychotics [29,31,32]. The switching study from olanzapine to risperidone by Takahashi and colleagues also reported a significant increase in plasma prolactin concentrations after the medication switch [33]. However, the findings of a recent study in patients with first episode psychosis indicated that such reproductive adverse events can occur even when prolactin levels are normal [34]. While the mechanisms underlying such reproductive adverse events in antipsychotic-treated patients are not yet clearly understood, it is well known that hyperprolactinemia can cause amenorrhea and galactorrhea.

In addition, weight gain has been commonly reported during treatment with most atypical antipsychotics, including olanzapine and risperidone [29]. However, our study did not find any significant differences in weight change before and after medication switch between the two groups (i.e. RIS-OLZ and OLZ-RIS groups). Notably, however, patients who were on olanzapine at baseline gained an average of 3.14 kg before switching to risperidone. In comparison, those who were initially on risperidone gained an average of 1.43 kg before switching to olanzapine.

The results of this study also confirmed that medication switch is a common practice in the management of schizophrenia [29]. More than one in three outpatients with schizophrenia who initiated treatment with either olanzapine or risperidone switched antipsychotic medication at least once during the 3-year follow-up in the normal course of care. In addition, more than half of the switchers also made subsequent switches (52.4% for patients who switched to olanzapine and 61.1% for patients who switched to risperidone). While medication switch constituted a logical and common treatment strategy for patients who did not respond adequately to the prescribed antipsychotic treatment or could not tolerate treatment-emergent adverse events, it may not always result in improvement, as indicated in our study. Although the results of our study confirmed that switching to olanzapine would be of benefit to patients who failed treatment with risperidone, the converse was not confirmed.

Our results need to be interpreted in the context of the following study limitations. Firstly, the W-SOHO study was originally designed and powered to compare clinical and economic outcomes between olanzapine and other antipsychotics. Comparison of the impact of switching antipsychotics between olanzapine and risperidone was a post hoc analysis and, consequently, the relevant subgroup sample size was small. This, in turn, might have affected the statistical results. Secondly, our study sample was initially drawn from 11,078 patients, who completed the study and had no more than one missing visits during follow-up (i.e., 64% of the baseline sample). Although the retention rate of 64% was relatively high given the study duration of three years and thereby the same size of study completers was still very large, the clinical prognosis could differ between study completers and those who were not. It is, however, unclear whether the inclusion of study completers only disproportionally influenced the outcomes of the OLZ-RIS and RIS-OLS groups. Thirdly, as the outcome assessments were not performed blind, we cannot exclude the possibility that the treating psychiatrists were likely to evaluate patients more favorably after the switch. Nevertheless, this bias is unlikely to differ between medications. In addition, as the clinical outcomes were assessed by the treating psychiatrists, the assessment could have been subjective and influenced by their prior knowledge and expectations (i.e., observer bias). This could be of particular concern in the absence of blinding if more favourable assessments were made towards their preferred treatment. Notably however, a previous study [35] explicitly investigated the observer bias in SOHO by comparing patient-and investigator-reported outcomes, and found no such bias. Fourthly, although our findings were adjusted for pre-switch clinical and demographic characteristics of patients when comparing the impact of switching on clinical outcomes, there could be unobserved differences between the treatment groups, which may confound our results. Finally, for patients who switched medication between visits, we assumed that clinical status was similar to that at the visit before the switch.

Conclusion

The results of the present study demonstrate that there is a more favorable treatment course in patients with schizophrenia who switched from risperidone to olanzapine. They were significantly more likely to remain on the medication longer and improve in symptom control, and significantly less likely to experience relapse, EPS and amenorrhea/galactorrhea, compared with those switched from olanzapine to risperidone. This suggests that while olanzapine can be an effective treatment option for patients requiring a switch from risperidone, treatment with risperidone may have limited benefits for those requiring a switch from olanzapine. Nevertheless, given the nature of observational study design and small sample size, additional studies are warranted.

Figure 1
1Kaplan-Meier estimates of time to (second) medication switch.

Figure 2
2Kaplan-Meier estimates of time to relapse.

Table 1
1Medication dose and reasons for discontinuation before and after switchingBefore switching
After switching

OLZ-RIS
(n=162)

RIS-OLZ
(n=136)

OLZ-RIS
(n=162)

RIS-OLZ
(n=136)

Dose (Mean ± SD)
10.1±5.3 4.7±3.0
4.6±3.2 10.9±5.9

Reason for discontinuation
(%) a

Lack of efficacy
41.3
59.1
36.0
34.0

Intolerability
29.7
31.8
15.7
20.0

Lack of compliance
19.4
23.5
19.1
16.0

Patient request
34.8
28.8
19.1
26.0

a

More than one reason allowed.
OLZ-RIS: Patients who switched from olanzapine to risperidone.
RIS-OLZ: Patients who switched from risperidone to olanzapine.

Table 2
2Patient characteristics before switching from
risperidone to olanzapine and from olanzapine to
risperidone

OLZ-RIS
(n=162)

RIS-OLZ
(n=136)

Age (years) a
37.2 ± 13.3
36.5 ± 12.5

Male (%) b
51.2
51.9

Age at first service contact for
schizophrenia (years) a

27.6 ± 10.3
27.9 ± 10.5

Duration of illness (years) a
9.7 ± 10.1
8.8 ± 8.9

CGI-SCH overall a
3.5 ± 1.2
3.6 ± 1.2

CGI-SCH positive a
3.0 ± 1.4
3.0 ± 1.4

CGI-SCH negative a
3.1 ± 1.3
3.2 ± 1.3

CGI-SCH cognitive a
2.8 ± 1.3
3.1 ± 1.3

CGI-SCH depressive a
2.7 ± 1.3
3.0 ± 1.4

Current alcohol abuse (%) b, *
5.0
1.5

Current substance abuse (%) b
2.5
3.7

Being hostile (%) b
9.9
11.0

Having a spouse/partner (%) b
30.4
24.2

Living independently (%) b
55.9
49.6

Being socially active (%) b
80.1
77.8

Being employed (%) b
29.8
25.2

a Data presented as mean (SD), t-test employed.
b Data presented as %, chi-test employed.
* Significant at p<0.05.
OLZ-RIS: Patients who switched from olanzapine to risperidone.
RIS-OLZ: Patients who switched from risperidone to olanzapine.
CGI-SCH: Clinical Global Impression-Schizophrenia.

Table 3
3Change in clinical status before and after switchingBefore switching
After switching

OLZ-RIS (n=162)
RIS-OLZ (n=136)
OLZ-RIS (n=162)
RIS-OLZ (n=136)

Table 4 The
4impact on clinical outcomes of switching to
risperidone, compared with switching to olanzapine

Estimate 95% confidence
interval

P-values

Cox regression
Hazard ratio a

Medication switch
1.44
0.97, 2.12
0.070

Relapse
3.43
1.43, 8.26
0.006

Linear regression
Coefficient b

Change in CGI-SCH overall
score before and after
switching

0.29
0.06, 0.52
0.013

Change in weight (kg) before
and after switching

0.22
−1.37, 1.81
0.785

Logistic regression
Odds ratio c

EPS
4.02
1.49, 10.89
0.006

Loss of Libido
1.77
0.93, 3.37
0.083

Impotence/sexual dysfunction
1.93
0.95, 3.94
0.070

Amenorrhea/galactorrhea
8.99
2.30, 35.13
0.002

a

A hazard ratio of more than 1 indicates an increased risk of medication
switch or relapse, compared with patients switched to olanzapine.

b

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AcknowledgementsThe W-SOHO study was funded by Eli Lilly and Company. The statements contained in this paper are solely those of the authors, and no endorsement by Eli Lilly and Company should be inferred.Competing interests JH is a consultant for Eli Lilly and Company. DN, JK and MD are employees of Eli Lilly and Company. Josep Maria Haro received honoraria for lecturing and consultancy (including attending advisory boards) from Eli Lilly and Company, AstraZeneca, and Lundbeck. Roberto Brugnoli received honoraria for lecturing and consultancy from the manufacturers of several antipsychotic agents including Eli Lilly and Company.Authors' contributions JH conducted the statistical analysis and drafted the manuscript. DN, RB, JK, MD and JMH participated in the design of the present study and provided critical review of the manuscript. All authors have read and approved the final manuscript.
Economic outcomes associated with switching individuals with schizophrenia between risperidone and olanzapine: findings from a large US claims database. Z Zhao, M Namjoshi, B L Barber, D L Loosbrock, S L Tunis, B Zhu, A Breier, CNS Drugs. 18Zhao Z, Namjoshi M, Barber BL, Loosbrock DL, Tunis SL, Zhu B, Breier A: Economic outcomes associated with switching individuals with schizophrenia between risperidone and olanzapine: findings from a large US claims database. CNS Drugs 2004, 18:157-164.

Variations in prescribing atypical antipsychotic drugs in primary care: cross-sectional study. D M Ashcroft, M Frischer, J Lockett, S R Chapman, Pharmacoepidemiol Drug Saf. 11Ashcroft DM, Frischer M, Lockett J, Chapman SR: Variations in prescribing atypical antipsychotic drugs in primary care: cross-sectional study. Pharmacoepidemiol Drug Saf 2002, 11:285-289.

Prescription of antipsychotic drugs by office-based physicians in the United States. R C Hermann, D Yang, S L Ettner, S C Marcus, C Yoon, M Abraham, Psychiatr Serv. 53Hermann RC, Yang D, Ettner SL, Marcus SC, Yoon C, Abraham M: Prescription of antipsychotic drugs by office-based physicians in the United States, 1989-1997. Psychiatr Serv 2002, 53:425-430.

Switching to olanzapine after unsuccessful treatment with risperidone during the first episode of schizophrenia: an open-label trial. H Takahashi, M Kamata, K Yoshida, J Ishigooka, H Higuchi, J Clin Psychiatry. 67Takahashi H, Kamata M, Yoshida K, Ishigooka J, Higuchi H: Switching to olanzapine after unsuccessful treatment with risperidone during the first episode of schizophrenia: an open-label trial. J Clin Psychiatry 2006, 67:1577-1582.

Evidence for the effectiveness of olanzapine among patients nonresponsive and/or intolerant to risperidone. M R Dossenbach, P Kratky, M Schneidman, S L Grundy, S Metcalfe, G D Tollefson, R H Belmaker, J Clin Psychiatry. 622SupplDossenbach MR, Kratky P, Schneidman M, Grundy SL, Metcalfe S, Tollefson GD, Belmaker RH: Evidence for the effectiveness of olanzapine among patients nonresponsive and/or intolerant to risperidone. J Clin Psychiatry 2001, 62 (Suppl 2):28-34.

Switching from risperidone to olanzapine in a one-year, randomized, open-label effectiveness study of schizophrenia. D E Faries, H Ascher-Svanum, A W Nyhuis, B J Kinon, Curr Med Res Opin. 24Faries DE, Ascher-Svanum H, Nyhuis AW, Kinon BJ: Switching from risperidone to olanzapine in a one-year, randomized, open-label effectiveness study of schizophrenia. Curr Med Res Opin 2008, 24:1399-1405.

The european schizophrenia outpatient health outcomes (SOHO) study: rationale, methods and recruitment. J M Haro, E T Edgell, P B Jones, J Alonso, S Gavart, K J Gregor, P Wright, M Knapp, Acta Psychiatr Scand. 107Haro JM, Edgell ET, Jones PB, Alonso J, Gavart S, Gregor KJ, Wright P, Knapp M: The european schizophrenia outpatient health outcomes (SOHO) study: rationale, methods and recruitment. Acta Psychiatr Scand 2003, 107:222-232.

Effectiveness of antipsychotic treatment for schizophrenia: 6-month results of the Pan-European Schizophrenia Outpatient Health Outcomes (SOHO) study. J M Haro, E T Edgell, D Novick, Acta Psychiatr Scand. 111Haro JM, Edgell ET, Novick D, et al: Effectiveness of antipsychotic treatment for schizophrenia: 6-month results of the Pan-European Schizophrenia Outpatient Health Outcomes (SOHO) study. Acta Psychiatr Scand 2005, 111:220-231.

Response and relapse in patients with schizophrenia treated with olanzapine, risperidone, quetiapine, or haloperidol: 12-month follow-up of the Intercontinental Schizophrenia Outpatient Health Outcomes (IC-SOHO) study. M Dossenbach, C Arango-Davila, Silva Ibarra, H Landa, E Aguilar, J Caro, O Leadbetter, J Assuncao, S , J Clin Psychiatry. 66Dossenbach M, Arango-Davila C, Silva Ibarra H, Landa E, Aguilar J, Caro O, Leadbetter J, Assuncao S: Response and relapse in patients with schizophrenia treated with olanzapine, risperidone, quetiapine, or haloperidol: 12-month follow-up of the Intercontinental Schizophrenia Outpatient Health Outcomes (IC-SOHO) study. J Clin Psychiatry 2005, 66:1021-1030.

Worldwide-Schizophrenia Outpatient Health Outcomes (W-SOHO): baseline characteristics of pan-regional observational data from more than 17,000 patients. J Karagianis, D Novick, J Pecenak, J M Haro, M Dossenbach, T Treuer, W Montgomery, R Walton, A J Lowry, Int J Clin Pract. 63Karagianis J, Novick D, Pecenak J, Haro JM, Dossenbach M, Treuer T, Montgomery W, Walton R, Lowry AJ: Worldwide-Schizophrenia Outpatient Health Outcomes (W-SOHO): baseline characteristics of pan-regional observational data from more than 17,000 patients. Int J Clin Pract 2009, 63:1578-1588.

APA: Diagnostic and statistical manual of mental disorders. APAWashington, DCFourthth editionAPA: Diagnostic and statistical manual of mental disorders. Fourthth edition. Washington, DC: American Psychiatric Association (APA); 1994.

WHO: The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines. World Health OrganisationGeneva, SwitzerlandWHO: The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines. Geneva, Switzerland: World Health Organisation (WHO); 1992.

The clinical global impressionschizophrenia scale: a simple instrument to measure the diversity of symptoms present in schizophrenia. J M Haro, S A Kamath, S Ochoa, D Novick, K Rele, A Fargas, M J Rodriguez, R Rele, J Orta, A Kharbeng, Acta Psychiatr Scand Suppl. 416Haro JM, Kamath SA, Ochoa S, Novick D, Rele K, Fargas A, Rodriguez MJ, Rele R, Orta J, Kharbeng A, et al: The clinical global impression- schizophrenia scale: a simple instrument to measure the diversity of symptoms present in schizophrenia. Acta Psychiatr Scand Suppl 2003, 416:16-23.

Remission and relapse in the outpatient care of schizophrenia: three-year results from the schizophrenia outpatient health outcomes study. J M Haro, D Novick, D Suarez, J Alonso, J P Lepine, M Ratcliffe, J Clin Psychopharmacol. 26Haro JM, Novick D, Suarez D, Alonso J, Lepine JP, Ratcliffe M: Remission and relapse in the outpatient care of schizophrenia: three-year results from the schizophrenia outpatient health outcomes study. J Clin Psychopharmacol 2006, 26:571-578.

Assessment of strategies for switching patients from olanzapine to risperidone: a randomized, open-label, rater-blinded study. R Ganguli, J S Brar, Mahmoud R Berry, S A Pandina, G J , BMC Med. 617Ganguli R, Brar JS, Mahmoud R, Berry SA, Pandina GJ: Assessment of strategies for switching patients from olanzapine to risperidone: a randomized, open-label, rater-blinded study. BMC Med 2008, 6:17.

Effectiveness of olanzapine, quetiapine, risperidone, and ziprasidone in patients with chronic schizophrenia following discontinuation of a previous atypical antipsychotic. T S Stroup, J A Lieberman, J P Mcevoy, M S Swartz, S M Davis, R A Rosenheck, D O Perkins, Rse Keefe, C E Davis, J Severe, Am J Psychiatry. 163Stroup TS, Lieberman JA, McEvoy JP, Swartz MS, Davis SM, Rosenheck RA, Perkins DO, Keefe RSE, Davis CE, Severe J, et al: Effectiveness of olanzapine, quetiapine, risperidone, and ziprasidone in patients with chronic schizophrenia following discontinuation of a previous atypical antipsychotic. Am J Psychiatry 2006, 163:611-622.

Risperidone versus olanzapine for schizophrenia. Cochrane database of systematic reviews cochrane database of systematic reviews. M B Jayaram, P Hosalli, T S Stroup, doi:005210.001002/14651858.CD14005237. pub1465185225237ArtJayaram MB, Hosalli P, Stroup TS: Risperidone versus olanzapine for schizophrenia. Cochrane database of systematic reviews cochrane database of systematic reviews. 2006. doi:005210.001002/14651858.CD14005237. pub14651852. Issue 2:Art. No.: CD005237.

Double-blind comparison of olanzapine versus risperidone in the treatment of schizophrenia and other psychotic disorders. P V Tran, S H Hamilton, A J Kuntz, J H Potvin, S W Andersen, C BeasleyJr, G D Tollefson, J Clin Psychopharmacol. 17Tran PV, Hamilton SH, Kuntz AJ, Potvin JH, Andersen SW, Beasley C Jr, Tollefson GD: Double-blind comparison of olanzapine versus risperidone in the treatment of schizophrenia and other psychotic disorders. J Clin Psychopharmacol 1997, 17:407-418.

Hospitalization rates associated with olanzapine, risperidone, and haloperidol treatment in patients with schizophrenia: Results from a U.S. randomized controlled trial. M Namjoshi, C Young, L Huang, Eur Neuropsychopharmacol. 12315Namjoshi M, Young C, Huang L, et al: Hospitalization rates associated with olanzapine, risperidone, and haloperidol treatment in patients with schizophrenia: Results from a U.S. randomized controlled trial. Eur Neuropsychopharmacol 2002, 12:S315.

Effect of switching antipsychotics on antiparkinsonian medication use in schizophrenia: population-based study. S Park, D Ross-Degnan, A S Adams, J Sabin, P Kanavos, S B Soumerai, Br J Psychiatry. 187Park S, Ross-Degnan D, Adams AS, Sabin J, Kanavos P, Soumerai SB: Effect of switching antipsychotics on antiparkinsonian medication use in schizophrenia: population-based study. Br J Psychiatry 2005, 187:137-142.

Costs of schizophrenia. M Knapp, Br J Psychiatry. 171Knapp M: Costs of schizophrenia. Br J Psychiatry 1997, 171:509-518.

Patient compliance with drug therapy in schizophrenia. Economic and clinical issues. E Lindstrom, K Bingefors, Pharmacoeconomics. 18Lindstrom E, Bingefors K: Patient compliance with drug therapy in schizophrenia. Economic and clinical issues. Pharmacoeconomics 2000, 18:106-124.

Efficacy and extrapyramidal side-effects of the new antipsychotics olanzapine, quetiapine, risperidone, and sertindole compared to conventional antipsychotics and placebo. A meta-analysis of randomized controlled trials. S Leucht, G Pitschel-Walz, D Abraham, W Kissling, Schizophr Res. 35Leucht S, Pitschel-Walz G, Abraham D, Kissling W: Efficacy and extrapyramidal side-effects of the new antipsychotics olanzapine, quetiapine, risperidone, and sertindole compared to conventional antipsychotics and placebo. A meta-analysis of randomized controlled trials. Schizophr Res 1999, 35:51-68.

Risperidone (Risperdal): clinical experience with a new antipsychosis drug. N A Keks, C Culhane, Expert Opin Investig Drugs. 8Keks NA, Culhane C: Risperidone (Risperdal): clinical experience with a new antipsychosis drug. Expert Opin Investig Drugs 1999, 8:443-452.

Practice guideline for the treatment of patients with schizophrenia, second edition. A F Lehman, J A Lieberman, L B Dixon, T H Mcglashan, A L Miller, D O Perkins, J Kreyenbuhl, Am J Psychiatry. 161Lehman AF, Lieberman JA, Dixon LB, McGlashan TH, Miller AL, Perkins DO, Kreyenbuhl J: Practice guideline for the treatment of patients with schizophrenia, second edition. Am J Psychiatry 2004, 161:1-56.

Three-year antipsychotic effectiveness in the outpatient care of schizophrenia: observational versus randomized studies results. J M Haro, D Suarez, D Novick, J Brown, J Usall, D Naber, Eur Neuropsychopharmacol. 17Haro JM, Suarez D, Novick D, Brown J, Usall J, Naber D: Three-year antipsychotic effectiveness in the outpatient care of schizophrenia: observational versus randomized studies results. Eur Neuropsychopharmacol 2007, 17:235-244.

Frequency of sexual dysfunction and other reproductive sideeffects in patients with schizophrenia treated with risperidone, olanzapine, quetiapine, or haloperidol: the results of the EIRE study. J Bobes, Apmp Garc, J Rejas, Hern Ndez, G Garcia-Garcia, M Rico-Villademoros, F Porras, A , J Sex Marital Ther. 29Bobes J, Garc APMP, Rejas J, Hern Ndez G, Garcia-Garcia M, Rico-Villademoros F, Porras A: Frequency of sexual dysfunction and other reproductive side- effects in patients with schizophrenia treated with risperidone, olanzapine, quetiapine, or haloperidol: the results of the EIRE study. J Sex Marital Ther 2003, 29:125-147.

Olanzapine vs. Other antipsychotics in actual out-patient settings: six months tolerability results from the European schizophrenia Out-patient health outcomes study. M Lambert, J M Haro, D Novick, E T Edgell, L Kennedy, M Ratcliffe, D Naber, Acta Psychiatr Scand. 111Lambert M, Haro JM, Novick D, Edgell ET, Kennedy L, Ratcliffe M, Naber D: Olanzapine vs. Other antipsychotics in actual out-patient settings: six months tolerability results from the European schizophrenia Out-patient health outcomes study. Acta Psychiatr Scand 2005, 111:232-243.

Switching between second-generation antipsychotics: why and how?. M Edlinger, S Baumgartner, N Eltanaihi-Furtmuller, M Hummer, W W Fleischhacker, CNS Drugs. 19Edlinger M, Baumgartner S, Eltanaihi-Furtmuller N, Hummer M, Fleischhacker WW: Switching between second-generation antipsychotics: why and how? CNS Drugs 2005, 19:27-42.

Reducing the burden of side effects during longterm antipsychotic therapy: the role of "switching" medications. P J Weiden, P F Buckley, J Clin Psychiatry. 686SupplWeiden PJ, Buckley PF: Reducing the burden of side effects during long- term antipsychotic therapy: the role of "switching" medications. J Clin Psychiatry 2007, 68(Suppl 6):14-23.

Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. J A Lieberman, T S Stroup, J P Mcevoy, M S Swartz, R A Rosenheck, D O Perkins, R S Keefe, S M Davis, C E Davis, B D Lebowitz, N Engl J Med. 353Lieberman JA, Stroup TS, McEvoy JP, Swartz MS, Rosenheck RA, Perkins DO, Keefe RS, Davis SM, Davis CE, Lebowitz BD, et al: Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N Engl J Med 2005, 353:1209-1223.

Switching to risperidone after unsuccessful treatment of olanzapine in the first-episode schizophrenia: an open trial. H Takahashi, K Yoshida, J Ishigooka, H Higuchi, Prog Neuropsychopharmacol Biol Psychiatry. 30Takahashi H, Yoshida K, Ishigooka J, Higuchi H: Switching to risperidone after unsuccessful treatment of olanzapine in the first-episode schizophrenia: an open trial. Prog Neuropsychopharmacol Biol Psychiatry 2006, 30:1067-1072.

Sexual dysfunction and hormonal changes in first episode psychosis patients on olanzapine or risperidone. M Van Bruggen, T Van Amelsvoort, L Wouters, P Dingemans, L De Haan, D Linszen, Psychoneuroendocrinology. 34van Bruggen M, van Amelsvoort T, Wouters L, Dingemans P, de Haan L, Linszen D: Sexual dysfunction and hormonal changes in first episode psychosis patients on olanzapine or risperidone. Psychoneuroendocrinology 2009, 34:989-995.

Methodological aspects in the assessment of treatment effects in observational health outcomes studies. J M Haro, S Kontodimas, M A Negrin, M Ratcliffe, D Suarez, F Windmeijer, 10.1186/1471-244X-12-218Appl Health Econ Health Policy. 5Haro JM, Kontodimas S, Negrin MA, Ratcliffe M, Suarez D, Windmeijer F: Methodological aspects in the assessment of treatment effects in observational health outcomes studies. Appl Health Econ Health Policy 2006, 5:11-25. doi:10.1186/1471-244X-12-218

Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution. Hong, BMC Psychiatry. 12218Clinical consequences of switching from olanzapine to risperidone and vice versa in outpatients with schizophrenia: 36-month results from the worldwide schizophrenia outpatients health outcomes (W-SOHO) study. Submit your manuscript at www.biomedcentral.com/submitCite this article as: Hong et al.: Clinical consequences of switching from olanzapine to risperidone and vice versa in outpatients with schizophrenia: 36-month results from the worldwide schizophrenia outpatients health outcomes (W-SOHO) study. BMC Psychiatry 2012 12:218. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit