Edgar Schiebel
email: edgar.schiebel@ait.ac.at

Dirk Holste
email: dirk.holste@ait.ac.at

Ivana Roche
email: ivana.roche@inist.fr

Dominique Besagni
email: dominique.besagni@inist.fr

Claire François
email: claire.francois@inist.fr

IDENTIFICATION OF EMERGING TECHNOLOGIES THROUGH PATHWAY-III ANALYSIS

Early recognition of emerging technologies is very important for keeping or gaining a leading role in the markets of companies, for research organisations to stay in the front end of research as well as for national or international funding agencies to promote the right future technologies. We propose advanced methods and algorithms based on bibliometric analysis applied on large-scale databases for scientific publications in order to identify emergent technologies. In the PROM-TECH project we demonstrated in a proof-of-concept approach the possible and reliable detection of growing research activity for a variety of technological fields. Building on this work, in ongoing investigations we analysed in-depth research progress for single technologies defined by classification categories of the PASCAL database.

Using keywords that addressed a more disaggregated level of a technology, however, allows for in-depth analysis of selected technology fields. We constructed a diffusion model of three diffusion stages to establish a framework for the arrangement of keywords according to their history. We statistically estimated the parameters of the diffusion model for two successive time periods of four years and were thus able to model the development of "keyword pathways". Pathways were defined as the change of a keyword from one stage to the next during subsequent time periods. We showed that technological breakthroughs in technologies could be verified by related keywords changing from stage-1 of the diffusion model to stage-3 (which we termed pathway-III). Hence pathway-III analysis was strongly indicative of technological breakthroughs in emerging technologies.

Here we extend the analysis already conducted for single technological fields to the comprehensive survey of all 45 identified fields in PROM-TECH. We identify and extract keywords related to emerging technologies through a pathway-III analysis and show that it reveals new promising perspectives for emerging technologies.

Co-word maps demonstrate if predefined technology classes reflect a coherent emergence of term combinations or new combinations of terms do emerge cross over predefinitions. We could show that new emerging fields tend to combine research findings cross over many technology fields.

Introduction

An enduring problem in the identification and development-tracking of new and growing research topics, as well as emerging technologies, lies in their proper and fast recognition when lack of expert knowledge and short time windows practically constrains any detailed analyses. Additionally, emergence comes very often stealthy and hardly noticeable, and very often existing research findings are a matter of interdisciplinarity and therefore hardly recognisable by any expert. Advanced methods and algorithms based on bibliometric analysis hold the promise to purposefully exploit large-scale databases for scientific publications in order to identify emergent technologies [START_REF] Takeda | Optics: a bibliometric approach to detect emerging research domains and intellectual bases[END_REF][START_REF] Shibata | Detecting emerging research fronts based on topological measures in citation networks of scientific publications[END_REF][START_REF] Roche | Identification and Characterization of Technological Topics in the Field of Molecular Biology[END_REF].

In previously published works, we demonstrated in a proof-of-concept approach the possible and reliable detection of growing research activity for a variety of technological fields (PROM-TECH, 2007), which bundled extracted topics in high aggregated levels like "Optoelectronic devices" or "Semiconductors". Building on this work in ongoing investigations we analysed in-depth research progress for single technologies (Schiebel E. et al., 2009).

Our research has generally shown that the definition of a technological field is tied to the nature of the underlying research and very context-dependent. Because new technological fields are still in a status nascendi, where new research findings are increasingly expressed, labelled and diffuse with non-standard terminology within the research community, they are neither comprehensively defined nor extracted by simple retrieval techniques with predefined technology classes. Using keywords that addressed a more disaggregated level of a technology, however, allows for in-depth analysis of selected technology fields. Keywords of publications are matter-of-factly related to effects, methods, materials, devices or applications of the natural sciences. We thereby noticed that such keywords occurred in articles of clearly different technology fields and that they possessed a "historical trait", i.e., they originated from a home technology and diffused to other technology fields. We constructed a diffusion model of three diffusion stages to establish a framework for the arrangement of keywords according to their history [START_REF] Roche | Identification and Characterization of Technological Topics in the Field of Molecular Biology[END_REF]. Parameters of the diffusion model were statistically estimated for two successive time periods and thus a model for the identification of "keyword pathways" was built. Pathways were defined as the change of a keyword from one stage to the next during subsequent time periods. We showed that technological breakthroughs in technologies, e.g., the emission of bright white light of LEDs, could be verified by related keywords changing from stage-1 of the diffusion model to stage-3 (which we termed "pathway-III"). Hence pathway-III analysis was strongly indicative of technological breakthroughs in emerging technologies.

Here we extend the analysis already conducted for single technological fields to the comprehensive survey of all 45 fields identified in PROM-TECH. We extract keywords related to emerging technologies through a pathway-III analysis and show that it reveals new promising perspectives for emerging technologies.

Up to now we were only able to identify growth in pre-classified technology fields or pathways of single terms of predefined technology classes. In this paper we identified all terms of pathway-III for all 45 technology fields and examined their interrelations by their cooccurrence in publications cross over all fields. Co-word maps demonstrate if predefined technology classes reflect a coherent emergence of term combinations or new combinations of terms do emerge beyond established fields.

Data

Data related to the 45 identified fields in PROM-TECH have been extracted from the corresponding classification categories of the PASCAL bibliographic database for two time periods: between 2005 and 2006 (P1) and between 2007 and 2009 (P2). Longer time periods were not possible because of changes in the database production.

The second period includes the years 2007 and 2008 and only partially 2009. We wanted to include the newest publications. As it was not possible to divide a year for some equal 2.5 years periods we took these two unequal periods nevertheless. It was not a methodological problem, because we did not compare absolute quantities for the periods but relative.

For all the 45 considered technological fields, the PASCAL database query delivered 12,712 documents. In the first period we had 4,884 documents and 12,047 different keywords among which 5,032, (41%) appear less than 3 times. The second period had 7,828 articles and 14,306 terms among which 7,180 (50%) occur less than 3 times.

In Table 1, we detail the 45 technology fields, their definition, theirs corresponding PASCAL categories and, finally, the number of articles obtained for each considered time period.

Methodology

We used a diffusion model to build a framework for the classification of keywords in different stages of development. The next step was the classification of the keywords according to diffusion stages for the mentioned two periods. The migration of terms from one stage in period 1 to another stage in period two marked so called pathways. These pathways described the career of keywords. The migration of terms from a stage of genesis in period one to a stage of high diffusion in stage two was defined as emergence. As terms described only very singular characteristics of a technology, we had to recombine these terms according to their usage in the publications. Firstly we draw a co-word map with the software BibTechMon TM . It revealed the coherence of terms usage related to classified categories on one hand and autonomous coherence by the usage of the authors. We sharpened the picture of coherence by a cluster analysis. We did this in two ways. Firstly the clustering of cooccurrences of pathway-III terms with each other in publications gave us an idea about an emerging technology as a bunch of different characteristics. In a second so called diachronic cluster analysis we combined publications where such pathway-III terms occurred and examined the evolution of the emerging technologies over the two time periods.

Diffusion model

To build the model we start with the idea that technologies have their roots in an invention which was created by one person or a small group of scientists who use specific terms for describing it. After a specific invention is published, the respective knowledge is available to other scientists. If scientists are inspired by the new ideas, they make use of the new theories and technologies, publish them and add their own ideas. At that stage, a new terminology is created and described in few publications. New terms are created or applied coming from other disciplines but get sometimes a new meaning. If the findings are of interest for other research groups they pick them up and the number of publication grow. In this second stage the findings are primarily interesting for researchers in the same discipline. That is why growth takes place in this area. If the findings are fundamental also for the use in another discipline the related terms diffuse over the scientific community and they occur in publications of many disciplines. Investigating the terminology of a suitable corpus of publications at a certain time span we can divide the terminology into three stages, which expresses an invention, new findings, growth in a discipline and diffusion in the scientific community:

(i.) STAGE 1: New terms created or adapted from other fields in connection with a specific considered technology (or discipline, etc.) can be found in a few publications. These terms occur with a low frequency in this considered field, and they form an exotic cluster.

(ii.) STAGE 2: Already established specific terms in the considered technology (or discipline, etc.) occur in this stage 2. We call the technology field, where a corpus of specific terms is established, the Home Technology for these terms.

(iii.) STAGE 3: This is the stage where we consider the so called cross section terms. Such terms are well established and applied in different technology fields. They are well understood not only in the considered field. They show a broad diffusion to other research or technology fields. One can detect a high number of articles in different technology fields using these terms.

We call this model of the three stages Diffusion Model. Therefore, the diffusion model identifies three categories of terms: terms unusual in a considered field, the established terms in a considered field, and cross section terms regarding to a considered field.

To be able to examine diffusion over different technologies we have to assign the so called Home Technology to each term. Terms occurring with a higher probability in a considered field than in another field are defined as such Home Technology terms. For defining these terms we used the 45 technology fields identified in the PROMTECH-Project. The probability is defined by the frequency (number of publications) of one term in a technology field divided by the absolute number of articles in this field. This probability is calculated for the same term for all technology fields in our corpus. The technology field with the highest probability is defined as the Home Technology for the considered term.

Secondly, we used a measure for the Cross Section Degree of a term in a selected technology.

For this purpose, we introduced the Gini Index (Gini Index, 2008), a measure of statistical dispersion most prominently used as a measure of inequality of income distribution or inequality of wealth distribution. The Gini Index varies from 0 to 1. In our case, 0 means a completely uniform distribution of all terms and indicates that each term of the considered field occurs in all other fields of our corpus. A Gini Index of 1 tells us that all terms of the considered field occur only in this field, which means that no term of the considered field occurs in another field. Therefore, more general terms have a lower Gini Index.

The bibliometric interpretation of the diffusion model with three stages allowed to assign each keyword to a diffusion stage: Stage 1: Birth seldom or unusual use of terms; Stage 2: growth of usage of a term in articles of the home technology; Stage 3: high number of articles and diffusion of terms to other technologies (high number of articles in different technologies).

We used a bibliometric filter to classify keywords according to different stages of the diffusion model, see Table 2. The filter was sensitive to 1) the number of articles; 2) the Gini index as a measure for the unevenness of the probability distribution of keywords over 45 technological fields; and 3) the relative frequency of terms for two time periods. A tracking procedure of keywords through the different stages for two periods revealed pathway-III significant keywords.

Pathways

We describe the dynamics of emerging technologies by pathways of keywords through diffusion stages from one period to the following one. Figure 1 illustrates the pathways. The dynamics of emerging technologies can be revealed by identifying the keywords that follow the mentioned pathways, see [START_REF] Besagni | Les émergences technologiques dans le domaine des dispositifs optoélectroniques : identification et caractérisation, paper presented at the séminaire[END_REF]. In this paper we concentrate on terms that follow the pathway-III. They are looked upon as keywords with the shortest career of emergence.

Co-word mapping

The interrelation of identified pathway-III terms are examined by a co-word analysis. For the visualisation and analysis we used the software BibTechMon TM . The core of the software is an algorithm to position related objects in a two dimensional space. The relation of terms in our case is defined by their pair wise co-occurrence in publications. These co-occurrences are normalized by the often used Jaccard index. The terms are arranged in a network structure by a spring model coming from mass point mechanics. In this way a term is positioned with respect to all other ones. A term in the centre of the network or a sub network has a more or less strong relation ship to many other nodes in the network graph, what is called a high centrality. In our case, established terms or very common terms are centred. New terms and unusual terms are positioned in the periphery. A bunch of terms that are cited often together cross over many publications form up in a sub network.

Diachronic cluster analysis

In a last step we re-incorporate the keywords into their context by a diachronic clustering of corresponding documents [START_REF] Polanco | An Integrated Environment for Clustering and Mapping Analysis on Science and Technology[END_REF]). The diachronic cluster analysis is realized with the help of a clustering tool applying first a non-hierarchical clustering algorithm and then a principal component analysis to map the obtained clusters. This tool is implemented in the information analysis platform Stanalyst ® . Neurodoc [START_REF] Lelu | Hypertext paradigm in the field of information retrieval: A neural approach[END_REF][START_REF] Lelu | Modèles neuronaux pour l'analyse de données documentaries et textuelles[END_REF], the clustering tool we used, applies a non-hierarchical clustering algorithm, the axial K-means method, coming from the neuronal formalism of Kohonen's self-organizing maps, followed by a principal component analysis (PCA) in order to represent the obtained clusters on a 2-D map.

Our approach consists of:

(i.) splitting the corpus in two successive time periods, namely 2005-2006 and 2007-2009; (ii.) applying cluster analysis on the corpus of each period, in which documents are represented by the keywords existing in the bibliographic references;

(iii.) analysing the evolution between the two cluster sets and maps by examining the vocabulary related to the clusters of the two periods.

In order to analyse the evolution of the cluster vocabulary between the two considered periods, we build a comparison matrix pointing out the percent of keywords belonging to the second-period clusters and already existing in the first-period clusters. The cumulated percentage is also calculated for each second-period cluster. Using this matrix, we can identify different cluster behaviours: stability, fusion or splitting. Using the cluster maps, we can also detect status change of the clusters in the global network.

Results

The first step to obtain pathway-III terms was the application of the bibliometric filter to arrange the terms in the stages of diffusion. The distribution of terms by the number of publications reveals a high number of keywords occurring only once or twice. To avoid this noise we selected terms with at least 3 publications. A first categorisation in stage 3 was done by extracting keywords that occurred in publications that were assigned to many classification categories or to many technologies in our notation. We used a threshold of 0,9642 for the Gini Index in period 1 and 0,95556 in period 2. The criterion was to obtain 3 stages with an equal number of terms. This was arbitrary but pragmatic to construct a framework for a further analysis. The remaining two thirds of terms with a higher Gini Index (low diffusion over other technology fields) were separated by low and high frequency in stage 1 and stage 2, see Table 3. Based on this filter list of terms according to the diffusion stages have been generated in a database. According to the definitions of pathways in Figure 1 the lists of terms were examined on identical terms. The Table 4 shows the number of terms in each pathway. For example we calculated the number of keywords with pathway III by identifying the same terms in the list of stage 1 in the first period and in the list of terms of stage 3 in second period.

Pathway-III terms per technology

Pathway-III terms demonstrate shift from a new and rather exotic status in the first period to a status of high relevance in the second period for several technological fields. As a first approach we sorted the 45 technologies by the number of pathway-III terms, see Table 5. It is an astonishing fact that "Cardiovascular systems" with only 18 publications in period 1 and 53 publications in period 2 hold the highest number of pathway-III terms. The technology with the second highest number of pathway-III terms is "molecular biology, general", where we have 55 publications in period 1 and 81 publications in period 2. We found the lowest number of pathway-III terms for "ceramics" with one term and "surface treatment of metals" with two ones. A deeper look at two technologies with the highest number of pathway-III terms is given in the tables 6 and 7. These tables reflect pathway-III terms. We have to consider, that each of these keywords occurred in several technologies but with highest probability in the mentioned one. As we can see in figure 3 the terms of Cardiovascular systems do not form a sub network with a high density, but show very low co-occurrences. This means that there is not a significant combination of these terms in the period 2. The publications reveal that there are many articles dealing with models of blood flow: models with viscous flow in elastic tubes, injured endothelial wall, a rheological model for the inelastic blood rheology with viscoelastic fluid, application of parallel algorithm for the numerical simulation of the blood flow in human brain, coupling of an elastic structure with an incompressible fluid. Central terms in this research also occur in a couple of other technologies, which could be an interesting interdisciplinary combination. There are 13 pathway-III terms with Molecular biology as a home technology. Figure 3 together with a study of the relevant publications suggests, that there is no focus on an emerging issue in molecular biology visible by observation with pathway-III terms. The terms are spread over the landscape and different topics are stressed by the articles. The migration of these terms from stage 1 in period P1 to stage 3 in period P2 must have other reasons than a coherent development of a research issue in molecular biology.

Co-word map of pathway-III terms

Emerging technologies tend to appear as a bunch of activities that are closely related or recombined in a new manner. A good possibility to show this kind of interdependencies is given by co-word maps.

The key question, we wanted to investigate with co word mapping is: Do technologies defined by classification categories reveal emerging new (sub)technologies or do we have much more significant new combinations that form a new emerging technology, that has not been identified before and established by a classification code.

The co-word map of all pathway-III terms is shown in Figure 2. In this picture we demonstrate the interrelationships between the terms with a focus on the technology fields defined by the classification codes.

The map shows a big core of interrelated terms and some peripheral single topics. There is a remarkable interconnection between the terms that could not be expected in this form. We expected more separated sub networks because of the different nature of the selected 45 technologies. It seems that these emerging topics do not represent particular and separated research but reflect high interdisciplinarity in the combination of methods, materials, models, etc.

The 45 technologies are represented by the colours in the map. A deeper view at each of the technologies shows that there is no significance that one of them forms a coherent sub network. "Photovoltaic cell" takes a very prominent part with 46 articles and 25 co-pathway-III terms. Therefore it can be assumed that this is an application with the highest emergence in our investigation. The second one is "Drug dispensation" with 25 articles and 14 co-pathway-III terms. To demonstrate the interrelationship and the (non)coherence of terms of the technologies defined by the classification codes we highlighted the terms with the assigned home technology as shown in Figure 3. As it has already been mentioned before, the co-occurrences of such terms in articles is very weak. It should be expected that emerging technologies tend to have a broad interest of a growing number of researchers and new upcoming terminology is used in publications what usually leads to high co-occurrences between such terms. We could not show this for the predefined technologies.

For the technology field "Cardiovascular system" there is a little higher interconnection of the terms by co-occurrences in articles but all other fields have only a few interrelations. On the other hand the network density of the whole map is much higher (see Figure 2) than in the association of terms by the classified technology fields. This means, that research activities do form other bunches of term combinations.

We obtain a complete different picture, if we identify clusters of terms based on their interrelations as shown in Figure 2.

Cardiovascular system Molecular biology, general

Electrical energy Hydraulic engineering Co-word map of terms according to their co-occurrence in articles, circle: term, size proportional to number of articles, connection: Jaccard index of co-occurrences, period: 2007-2009, BibTechMon TM

Diachronic cluster analysis

The diachronic cluster analysis applied on the two periods P1 and P2, allows us to determine which topics of the second period have roots in the first one and which topics are new in the second period. The clustering algorithm produced 20 clusters involving 4,868 documents and 3,650 keywords in period P1, and 20 clusters relating 7,827 documents and 7,398 key-words in period P2. Let us notice that the important reduction of the number of keywords associated to the clusters in both periods, around 35% with respect to the initial number of terms obtained directly from the query results, does not entail a significant loss of information at the level of the documents. Indeed we had 99.7% and 100% of the initial information corpora in the clusters of respectively P1 and P2. We can see in Figures 5 and6 the obtained cluster maps for the two periods.

Comparing the cluster contents of the two maps we observe, first of all that a great number of clusters in the second period remain stable, coming predominantly from their homonym in the first period. They are: "Modelling", Manufacturing", "Vibration", "Biomedical imaging", "Light emitting diodes", "Microstructure" and "Microfluidics". The two others homonymous clusters, "Surface treatments" and "Modelling", have a different behaviour and are mainly formed by, respectively, one ("Thermo chemical treatment") and 3 ("Machining", "Vibration" and "Engineering design") more clusters, besides the homonymous cluster. These 9 homonymous clusters contribute to structure the networks in both periods.

In the first period we have, in the left side of the map, a great network gathering a set of clusters dealing with mechanical engineering ("Engineering design", "Modelling", "Vibrations" and "Diesel engine") and clusters related to electronics ("Light emitting diodes" and "Experimental study"). The cluster "Machining" links these two sub-networks. In the right upper side is located a little network dealing with chemical engineering and metallurgy, clusters "Thermo chemical treatment", "Surface treatments" and "Microstructure". The three clusters related to life sciences research topics ("Biomedical imaging", "Phonation" and "Inner ear") have isolated locations in the cluster map. In the second period map we find, in the half lower side, a big network corresponding to the restructuring of the two networks observed in the first period map. Indeed the splitting of the cluster "Machining" contributes to separate the mechanical engineering sub-network from the electronics one. Furthermore it facilitates its grouping with the small network dealing with chemical engineering and metallurgy. The consequence is the formation, in the second period, of this large network starting from mechanical engineering, in its right part, to chemical engineering in its left part going through metallurgy. In the left upper side of the map, appears a network of clusters dealing with electronics and coming from a single cluster ("Light emitting diodes") in the first period. Indeed, this cluster is at the origin of important new clusters: "Photodetectors", "Light emitting diode", "Nanostructured materials" and "Carbon nanotubes". This evolution shows mainly the increasing of optoelectronic devices and the emergence of new research topics related with nano-technologies. The life sciences are represented by two clusters still in isolated position ("Biomedical imaging" and "Eyes") but with size increase.

Convergence between diffusion model and diachronic cluster analysis

The comparative analysis of the results of the two approaches operated in this work, namely the diffusion model and the diachronic cluster analysis, reveals some quite interesting convergence points.

We study the location, on the P2 cluster map (Figure 6), of the pathway-III terms related to the 4 technologies given in figure 3 and we compare with the co-word maps presented in Figure 3: (i.) Cardiovascular system: the 17 pathway-III terms are grouped in the left side of the coword map and they are present in the 4 clusters of the right part of the biggest network ("Parallel mechanism", "Vibration", "Modelling" and "Modeling"). This concentration in clusters related to mechanical engineering shows the need in technical knowledge to manage medical issues, especially in fluid mechanics, concerning the modelling of blood flow. (ii.) Molecular biology: the 13 pathway-III terms are located in the right side of the coword map and they are present in the electronics network (mainly "Light emitting diodes", "Nanostructured materials" and Photodetector"), in the life science clusters ("Biomedical imaging" and "Eyes") and in the middle of the biggest network ("Manufacturing" and "Thin film"). This fact is reflected in some publications about bio-inspired nanostructured materials. (iii.) Electrical energy: the 12 pathway-III terms are also in the right side of the co-word map and they are present in the electronics network (mainly "Light emitting diodes", "Performance evaluation", "Nanostructured materials" and "Carbon nanotubes") and in the left part of the biggest network ("Microstructure", "Surface treatments" and "Manufacturing"). (iv.) Hydraulic engineering: the 12 pathway-III terms are dispersed in the co-word map with some groupings in its left side. Some of them are present in two clusters ("Vibration" and "Modeling") located in the right part of the biggest network.

Conclusions

In the PROM-Tech project we developed a method to identify emerging technologies and applied it with the PASCAL bibliographic database. The study of the growth of the number of publications and an expert evaluation generated 45 promising emerging technologies from the point of view of expected research breakthroughs and high economic potential. These technologies had been defined on the basis of the classification categories assigned to each document by the database producer. On this level only highly aggregated already fields were visible and emergence was supposed to happen somewhere inside of this field. We performed a deeper view inside of these fields by analysing diffusion characteristics of terms of document keyword lists. In our first approaches we focused on single fields out of the 45 technologies and examined dynamics of keyword careers within the boundaries of one technology field. We observed that many research activities in the shape of terms were used in a timely dynamic and changing manner in a stable, growing or descending, number of the identified technology fields. We made the obvious assumption that such terms combine to emerging research as a bunch of activities cross over the predefined technologies. We extracted such activities by using a diffusion model over two periods and identified so called pathway-III terms that migrated from a stage of genesis to a stage of high diffusion to many research activities. These terms did not have high co-occurrence in publications when we focused them on their assignment to the predefined technologies. A co-word map revealed an intense interrelationship of the pathway-III terms with each other. Terms with a high centrality were Photovoltaic cell and Drug dispension. They are representatives of two important research areas one in engineering and one in medicine. Significant combinations of pathway-III terms by co-occurrences could be identified with a cluster analysis: Photovoltaic cell, Imagery, Laser deposition, Anodizing, Drug dispension and Diagnostic techniques, Planar technology and Optical characteristic as well as Energy method.

The evolution of the whole field with all keywords of the 45 technologies, obtained by the diachronic cluster analysis, showed still high dynamics in research about both, well known emerging technologies with, for example,the increasing of optoelectronic devices, and the emergence of new research topics related with nano-technologies. The life sciences are represented by two clusters ("Biomedical imaging" and "Eyes") still in isolated position in the map but with an increase of their size.

The appearance of pathway-III clusters in the whole evolution of the technology fields was studied by their location in the cluster map of period P2. The terms associated with "Cardiovascular system" are grouped in the left side of the co-word map and they are present in the 4 clusters of the right part of the biggest network ("Parallel mechanism", "Vibration", "Modelling" and "Modeling"). This concentration in clusters related to mechanical engineering shows the need in technical knowledge to manage medical issues, especially in fluid mechanics, concerning the modelling of blood flow. Pathway-III terms related to Molecular biology were present in the electronics network (mainly "Light emitting diodes", "Nanostructured materials" and Photodetector"), in the life science clusters ("Biomedical imaging" and "Eyes") and in the middle of the biggest network ("Manufacturing" and "Thin film"). This fact is reflected in some publications about bio-inspired nanostructured materials.

Figure 1 .

 1 Figure 1. Taxonomy of the migration pathways of keywords (research topics) through different stages of the diffusion model in two time periods, see Schiebel et al 2009

Figure 2 :

 2 Figure 2: Coword map and network indicators for pathway-III terms

Figure 3 :

 3 Figure 3: Co-word map of pathway-III terms -Four selected technologies highlighted

Figure 4 :

 4 Figure 4: Co-word map of pathway-III terms -Four selected clusters

Figure 5 :

 5 Figure 5: First period cluster map of the 45 technological fields

Figure 6 :

 6 Figure 6: Second period cluster map of the 45 technological fields

Table 1 :

 1 Definition of technology fields and number of publications in the two periods

	ID	Database classification	Technology field	Articles	Articles
		categories		P1: 2005	P2: 2007 -
				and 2006	2009

Table 2 :

 2 Bibliometric filter to classify terms by diffusion stages

			Stages of
	Indicator	Selection Criteria	Diffusion
	technology field	all 45	-
	minimum number of articles	more than 2 articles -
	broad diffusion: small Gini Index
	for low concentration of terms in
	different technology fields	a third of all terms	stage 3 terms
			stage 1 or
	low Diffusion: large Gini Index	the other two thirds	stage 2
		next third of all
	high frequency of publications	terms	stage 2 terms
	low frequency of publications	last third of all terms stage 1 terms

Table 3 .

 3 Filter to identify stages of diffusion for terms

	Indicator	Indicator value	Number of terms	Stages of diffusion
			P1: 2005-2006 P2: 2007-2009
	technology field	all 45	12,047	14,306
	minimum number of articles	more than 2 articles	5,032	7,180
		P1: Gini
	broad diffusion: small Gini Index	<0,9642 P2: Gini	1,679	2394	stage 3 terms
		<0.95556
		P1: Gini
	low Diffusion: large Gini Index	>0,9642 P2: Gini	3,353	4,786
		>0.95556
		P1: freq.>4
	high frequency	articles P2: freq. >4	1,670	2665	stage 2 terms
		articles
		P1: freq.<5
	low frequency	articles P2: freq.<5	1,683	2,121	stage 1 terms
		articles

Table 4 : Number of keywords in different pathways Pathways Number of keywords Percent of terms in

 4

	P2

Table 5 : Number of pathway-III terms per technology

 5

	Technology	Number of pathway-
		III terms
	Cardiovascular systems	17
	Molecular biology, general	13
	Hydraulic engineering	12
	Electrical energy	12
	Transistors	12
	Thermal energy, combustibles	11
	Molecular electronics	11
	Biotechnology	10
	Magnetics	10
	MEMS	9
	Electronic tubes	9
	Electronic circuits	9
	Thermal treatment of metals	9
	Optoelectronics	9
	Mechanical working of metals	8
	Information storage	8
	Electrical machines	8
	Mechanical engineering, general	8
	Road traffic	8
	Metallurgy, general	8
	Biophysics of tissues, general	6
	Soil mechanics	6
	Cell biology	6
	Electrical materials	6
	Mechanical motors	6

Table 6 : Pathway-III terms in Cardiovascular systems Keyword Number of articles in P1 Number of articles in P2 Number of technologies

 6

	Vibrations	3	22	11
	Adhesive joint	3	15	10
	Collagen	3	13	7
	Simple support	4	13	7
	Viscoelastic fluid	3	13	8
	Viscous fluids	4	12	8
	Euler equation	3	10	5
	Numerical stability	3	10	5
	Stokes flow	3	10	8
	Viscous flow	3	10	7
	Fiber orientation	3	9	6
	Parallel algorithm	4	9	6
	Penalty method	3	9	6
	Internal flow	3	8	5
	Distributed parameter system	3	7	6

Table 7 : Pathway-III terms in Molecular biology, general

 7

	Keyword	Number of	Number of	Number of
		articles in P1	articles in P2	technologies
	Photoinduced effect	4	23	8
	Scanning microscope	4	15	7
	Colloidal suspension	3	14	5
	Diffuse reflection	3	13	5
	Biotin	3	10	8
	pH	4	9	7
	Thiols	3	9	6
	Ellipsometry	4	8	7
	Optical losses	4	8	5
	Laser welding	4	6	4
	Stochastic equation	3	6	5
	Target tracking	3	5	5
	Focal length	3	4	4


M.D Tuija H Nieminen
email: tuija.nieminen@utu.fi

M.D Nora M Hagelberg

M.D Teijo I Saari

M.Sc Mikko Neuvonen

M.D Pertti J Neuvonen

M.D Kari Laine

M.D Klaus T Olkkola

Oxycodone concentrations are greatly increased by the concomitant use of ritonavir or lopinavir/ritonavir

Keywords: Oxycodone, Ritonavir, Lopinavir/ritonavir, Interaction, Pharmacokinetics, Pharmacodynamics Oxycodone, Ritonavir, Lopinavir/ritonavir, Interaction, Pharmacokinetics, Pharmacodynamics

Purpose: This study was aimed to investigate the effect of antivirals ritonavir and lopinavir/ritonavir on the pharmacokinetics and pharmacodynamics of oral oxycodone, a widely used opioid receptor agonist used in the treatment of moderate to severe pain.

Methods:

A randomized crossover study design with 3 phases at intervals of 4 weeks was conducted in 12 healthy volunteers. Ritonavir 300 mg, lopinavir/ritonavir 400/100 mg or placebo b.i.d. for 4 days was given to the subjects. On day 3, 10 mg oxycodone hydrochloride was administered orally. Plasma concentrations of oxycodone, noroxycodone, oxymorphone and noroxymorphone were determined for 48 h. Pharmacokinetic parameters were calculated with standard noncompartmental methods. Behavioural effects and experimental cold pain analgesia were assessed for 12 h. ANOVA for repeated measures was used for statistical analysis.

Results: Ritonavir and lopinavir/ritonavir increased the area under the plasma concentration-time curve of oral oxycodone by 3.0-(range 1.9-to 4.3-fold; P<0.001) and 2.6-fold (range 1.9-to 3.3-fold; P<0.001). The mean (± SD) elimination half-life prolonged after ritonavir and lopinavir/ritonavir from 3.6 ± 0.6 to 5.6 ± 0.9 h (P<0.001) and 5.7 ± 0.9 h (P<0.001), respectively. Both ritonavir (P<0.001) and lopinavir/ritonavir (P<0.05) increased the self-reported drug effect of oxycodone.

Conclusions: Ritonavir and lopinavir/ritonavir greatly increase the plasma concentrations of oral oxycodone in healthy volunteers and enhance its effect. When oxycodone is used clinically in patients during ritonavir and lopinavir/ritonavir treatment, reductions in oxycodone dose may be needed to avoid opioid-related adverse-effects.

INTRODUCTION

Pain is one of the most frequently reported symptoms associated with human immunodeficiency virus (HIV) infection. It has been estimated that in the developed world, depending on the stage of disease and study settings, 30-80% of people living with HIV/AIDS are affected with pain [1,2]. Pain experienced by HIV patients can be acute or chronic, and it is usually multifactorial in origin. HIV-associated pain can be due to e.g. HIV itself, antiretroviral treatment, opportunistic infections, HIV-associated cancers, rheumatic conditions or peripheral polyneuropathy [3][4][5]. Unfortunately, these painful conditions are often underrecognised and -treated.

Oxycodone is a semi-synthetic opioid agonist, which is effective in treating acute, cancer-related and chronic pain [6,7]. It is metabolized mainly via oxidative, but also via reductive pathways with only 10-14% being excreted in unchanged or conjugated forms in urine [8]. Oxycodone is principally a cytochrome P450 3A4/5 substrate, which makes it prone to the interactions with CYP3A inducers and inhibitors [9]. The main oxidative pathway includes N-demethylation to noroxycodone by CYP3A4/5 and further to noroxymorphone by CYP2D6 [9]. Approximately 11% of oxycodone is O-demethylated to oxymorphone by CYP2D6, which is further transformed to noroxymorphone by CYP3A4/5 and CYP2D6 [9]. Animal studies have given controversial results about the role of P-glycoprotein in the transport of oxycodone via blood-brain barrier (BBB ) [10,11]. However, the higher concentrations of oxycodone in rat and sheep brain tissue compared to plasma concentrations suggest that the influx protein is much more active than P-glycoprotein, which acts as an efflux protein limiting the concentrations of oxycodone in brain [12][13][14].

HIV protease inhibitors, such as ritonavir and lopinavir inflict the HIV-specific aspartic protease enzyme on processing the gag and gag-pol polyprotein precursor, which leads to the production of immature, noninfectious viral particles [15]. Ritonavir is a potent CYP3A, CYP2D6 and P-glycoprotein inhibitor [16][17][18]. It has been described to induce CYP1A2, CYP2B6, CYP2C9 and CYP2C19 [19,20]. Difficult dosing regimens, the development of resistance and adverse effects have restricted the use of ritonavir as an independent part of HIV treatment [21]. Lopinavir/ritonavir is a combination preparation, where lopinavir is combined with low-dose ritonavir to enhance its low bioavailability by favourable CYP3A inhibition [22].

Lopinavir/ritonavir has become a well-tolerated, effective and preferred protease inhibitor in the treatment of HIV. It is a substrate and inhibitor of CYP3A4 and to a lesser extent CYP2D6, and an inducer of CYP1A2, 2B6, 2C9 and 2C19 [23,24] which makes it prone to cytochrome P450-mediated interactions.

In previous studies it has been shown, that CYP3A inhibitors voriconazole [25], itraconazole [26] and telithromycin [27] increased the concentrations of oxycodone and oxymorphone, but only minor changes have been observed in the pharmacological action of oxycodone following single doses in healthy volunteers. As HIV patients are often affected with moderate to severe pain, there is an obvious need for the concomitant use of antivirals and strong opioids, such as oxycodone. The aim of this study was to investigate the effects of ritonavir and lopinavir/ritonavir on the pharmacokinetics and effects of oxycodone in healthy volunteers.

METHODS

Participants

The study protocol was approved by the Ethics Committee of the Hospital District of Southwest Finland and by the Finnish National Agency for Medicines and was and was thereby reported to the EudraCT clinical trial register. In view of our previous studies, it was calculated, that 10 subjects would be required to detect a 30% difference in the area under the oxycodone concentration-time curves (AUC 0-∞ ) at a level of significance P = 0.05 and power of 80% [25][26][27]. To allow a drop-out rate of 20%, we decided to recruit 12 subjects. Twelve healthy volunteers, four women and eight men (age 18-26 years; weight 52-84 kg) were studied according to the revised Declaration of Helsinki. Informed consent was obtained from all subjects before study screening. All participants were ascertained to be healthy by means of medical history and clinical examination, and they were tested for routine laboratory tests. Urine screens for drugs with abuse risk were negative and a 12-lead electrocardiogram (ECG) within normal limits. Pregnancy tests for women were negative. None of the participants were receiving any continuous medication including hormonal contraception and all were non-smokers. Women in need of contraception were instructed to use nonhormonal methods during the study. Finnish translation of Abuse Questions [28] was used to assess risk for drug abuse. The consumption of any product with potential CYP3A effect such as herbal remedies or grapefruit juice was forbidden for 4 weeks and all other drugs for 2 weeks before and during the study. Consumption of coffee, tee, alcohol and cola drinks were not allowed in the study facility during the study days. Genotyping for CYP2D6 was conducted by a two-step multiplex primer extension method [29]. This method enables the detection of 11 most relevant polymorphic positions, the definition of whole-gene deletion, duplication and the allele combination in case of gene duplication. The volunteers fasted for 8 h before oxycodone hydrochloride administration and received a standard meal 4 and 8 h after dosing. Drinking was allowed only during the meals. Adherence to the pre-treatment medication schedule was ensured with mobile phone text messages, which the volunteers sent to the investigator after taking each dose of pre-treatment taken outside the study facility.

Study design and drug dosing

Sampling and determination of drug concentrations

In the study facility, a cubital vein was cannulated for blood sampling. The samples (10 ml each) were collected into EDTA containing tubes before and 1, 1.5, 2, 3, 4, 5, 6, 8, 10, 12, 24, 48 h after the oxycodone hydrochloride dose. Plasma was separated within 30 min and stored at -70°C until analysis. Plasma concentrations of oxycodone, noroxycodone, oxymorphone and noroxymorphone were analyzed with the liquid chromatography-tandem mass spectrometric (LC-MS/MS) method as described earlier [30].

The lower limits of quantification (LLQ) were 0.1 ng/mL for oxycodone and oxymorphone, and 0.25 ng/mL for noroxycodone and noroxymorphone. The interday coefficient of variation was less than 6% for parent oxycodone and less than 13% for the metabolites at relevant plasma concentrations. The concentrations of ritonavir and lopinavir just before and 2 h after the administration of the dose of pretreatment on the day 3 were determined with a LC-MS/MS method [31]. Saquinavir was used as internal standard. The LLQ was 0.1 µg/ml for ritonavir and 0.4 µg/ml for lopinavir. The interday CVs were < 5.4% and < 7.0% for ritonavir and lopinavir, respectively, at relevant concentrations (n=3).

Pharmacokinetic analysis

The pharmacokinetics of oxycodone and its metabolites were determined by standard noncompartmental methods using a pharmacokinetic program WinNonlin (version 4.1, Pharsight Corporation, Mountain View, CA, USA). The peak plasma concentrations (C max ) and the corresponding C max times (t max ) were observed directly from the data. The elimination rate constant (k e ) was determined by log-linear regression. The elimination half-life t ½ was calculated from ln2/k e . The areas under the oxycodone, noroxycodone, oxymorphone and noroxymorphone concentration-time curves (AUC 0-∞ ) were calculated by trapezoidal rule with extrapolation to infinity. Whenever k e could not be estimated because of low plasma concentrations of the metabolite, AUC 0-48 was used instead of AUC 0-∞ . The metabolite-to-parent drug AUC ratios (AUC m /AUC p ) were calculated to compare the relative abundance of each metabolite.

Analysis of pharmacological effects

Behavioral effects

The behavioural measures were performed before and at 1, 2, 3, 4, 5, 6, 8, 10 and 12 h after oxycodone as described earlier [32]. Subjective assessment of drowsiness, deterioration of performance, self-reported drugeffect, pleasantness and nausea or vomiting was quantified using visual analog scales (VAS) and scored 0-100. Attributes assessed included alert/drowsy, good/poor performance, no/strong drug effect, unpleasant/pleasant feeling and no/extreme nausea or vomiting [33]. Maddox wing test was used to estimate the central coordination of extraocular muscles [34]; digit symbol substitution test (DSST) to estimate the central processing of sensory information [35]

Experimental pain

The analgesic effect was studied in an experimental cold pressor test, which is sensitive to opioid effects [37]. The subjects placed their left hand up to the wrist into the ice water bath (temperature 0-2ºC) for one minute and reported the first sensation of pain (cold pain threshold, CPT), which was measured in seconds with a stopwatch. The intensity of cold pain at 60 s (CPI) was assessed using numerical rating scale (NRS 0-100). The area under the drug effect-time curve (AUEC 0-12 ) from 0 to 12 hours was calculated with linear trapezoidal rule based on the observations by the use of the WinNonlin (version 4.1, Pharsight Corporation, Mountain View, CA, USA) software for all effect variables.

Statistics

The results are expressed as mean ± SD, except for t max , where medians with minimum and maximum values are reported. In figures, for clarity, mean ± SEM are given. Descriptive and comparative statistics were calculated with use of SYSTAT for Windows (version 10.2; Systat software, Richmond, CA, USA). The pharmacokinetic and pharmacodynamic variables were compared with the use of analysis of variance for repeated measures and a posteriori testing was performed with Tukey's test. Pharmacokinetic data was logarithmically transformed before statistical comparisons. Analysis of variance was used to determine the contributions of treatment, CYP2D6-genotype, gender, phase and sequence on overall variance. The t max was analyzed by Friedman's test and Wilcoxon signed rank test was used for pair wise comparisons. P was considered significant at the level of <0.05. We also calculated geometric mean ratios with 90% confidence intervals (CI) for the pharmacokinetic parameters. Bioequivalence (i.e., the lack of an interaction) was concluded, if the 90% CI of the geometric mean ratios (coadministration/oxycodone alone) were within the acceptance limit of 0.8 to 1.25.

Pharmacokinetic results

The pharmacokinetic results are presented in Figure 1 and Table 1. During the ritonavir and lopinavir phases, all of the calculated pharmacokinetic parameters for oxycodone were outside the bioequivalence acceptance limits (Table 1). Compared with placebo, ritonavir and lopinavir/ritonavir increased the oxycodone AUC 0-∞ (geometric mean ratio) by 3.0-fold (90% CI 2.6, 3.4; P<0.001) and 2.6-fold (90% CI 2.3, 2.9; P<0.001), respectively. Ritonavir increased the geometric mean ratio of oxycodone C max by 1.7-fold (90% CI 1.5, 2.0; P<0.001) and prolonged its elimination half-life from 3.6 ± 0.6 h to 5.6 ± 0.9 h (P<0.001).

Lopinavir/ritonavir increased the geometric mean ratio of oxycodone C max by 1.4-fold (90% CI 1.2, 1.7; P<0.05) and prolonged its elimination half-life to 5.7 ± 0.9 hours (P<0.001). Gender had no significant influence on the pharmacokinetic parameters of oxycodone.

Both ritonavir and lopinavir/ritonavir decreased the geometric mean ratio for noroxycodone AUC 0-48 to 0.49 (90%CI 0.43, 0.58, P<0.001) and 0.44 (90%CI 0.38, 0.50; P<0.001), respectively. Ritonavir alone had no effect on oxymorphone but lopinavir/ritonavir increased geometric mean ratio for oxymorphone AUC 0-48 by 2.7-fold (90%CI 2.0, 3.8; P<0.05). The geometric mean ratio for noroxymorphone AUC 0-48 was 0.19 (90%CI 0.15, 0.24; P<0.01) after ritonavir and 0.24 (90%CI 0.16, 0.35; P<0.01) after lopinavir/ritonavir, respectively. Four subjects during ritonavir phase and three subjects during lopinavir/ritonavir phase had no detectable concentrations of noroxymorphone during the study period 0-48 hours. After pre-treatment with ritonavir, plasma ritonavir concentrations (mean ± SD) measured ½ and 3 hours after the morning dose on day 3, were 3.2 ± 1.6 µg/ml and 8.7 ± 2.6 µg/ml. After lopinavir/ritonavir pretreatment, the corresponding plasma lopinavir concentrations were 9.0 ± 2.0 µg/ml and 11.3 ± 2.9 µg/ml and ritonavir concentrations 0.5 ± 0.3 µg/ml and 1.0 ± 0.5 µg/ml, respectively. All volunteers had measurable values of ritonavir and ritonavir/lopinavir before the morning dose during active phases indicating compliance to the dosing schedule.

Pharmacodynamic results

Results from behavioral and analgesic tests are shown in Fig. 2. The self-reported drug effect AUEC 0-12 increased significantly after ritonavir and lopinavir/ritonavir (P<0.001 and <0.05). Ritonavir increased also nausea or vomiting as measured by VAS (P<0.01), but no other statistically significant differences were observed in the pharmacological response to oxycodone.

The adverse effects reported are presented in Table 2. All three volunteers with vomiting were treated with 2 mg intravenous tropisetron 4, 8 or 12 h after oxycodone.

DISCUSSION

This study convincingly demonstrates that short-term administration of ritonavir and lopinavir/ritonavir strongly inhibits the CYP3A-mediated N-demethylation of oxycodone, as reflected by an approximately 2-3fold increase in oxycodone AUC 0-∞ and decrease in noroxycodone AUC 0-48 . When oxycodone was administered together with ritonavir or lopinavir/ritonavir, the mean concentration of oxycodone was still higher at 8 h than the C max during the control phase. These pharmacokinetic changes were accompanied by enhanced self-reported drug-effect after both antiviral treatments.

Ritonavir has been shown to be a strong inhibitor of CYP3A both in vitro [38,39] and in vivo [16,18,40,41]. In the present study ritonavir caused a 3-fold mean increase of oxycodone AUC 0-∞ , which is somewhat smaller than that observed after the azole antimycotic voriconazole (3.6-fold increase in oxycodone AUC 0-∞ ), but bigger than that observed for itrazonazole (2.4-fold increase) and telithromycin (1.8-fold increase) [25][26][27]. It can be speculated, although it seems doubtful, that the interaction between oxycodone and ritonavir would have been stronger, if a larger ritonavir dose would have been used as recommended by the manufacturer. However, because we wanted to minimize possible adverse effects in healthy volunteers, 300 mg b.i.d. was used and the treatment lasted only four days. This could lead to bias, as the steady-state was not reached [21]. Ritonavir has been reported to cause an enzyme induction after approximately 2 weeks administration, which can diminish the inhibitory effects [42,43].

Interestingly, lopinavir/ritonavir resulted in an almost equal alteration in oxycodone pharmacokinetics as compared to ritonavir. The clinically used maintenance dose of ritonavir is 600 mg b.i.d., which is recommended to be attained during 2 weeks to avoid adverse effects. During the 4-day pre-treatment our volunteers were given 300 mg of ritonavir b.i.d., which is three times higher than the dose of ritonavir during lopinavir/ritonavir phase. Still, the pharmacokinetics of oxycodone was very similar whether it was given together with ritonavir or lopinavir/ritonavir. A plausible explanation for the observed pharmacokinetic changes is the additional inhibitory effect by lopinavir. Lopinavir is a less potent inhibitor of CYP3A in vitro than ritonavir, but it might nonetheless contribute to net CYP3A inhibition in vivo during the treatment with 12 the combination of lopinavir and ritonavir. The dose-response curve for CYP3A inhibition by ritonavir can be so flat in vivo that despite a 3-fold dose reduction, it causes a maximal inhibition of CYP3A when combined with a 4-fold higher dose of lopinavir [44].

Ritonavir inhibits CYP2D6 in vitro, but to a lesser extent than it inhibits CYP3A [44,45]. In the present study, oxymorphone concentrations were increased during both antiviral phases indicating the increased significance of compensatory oxycodone metabolic route via CYP2D6 during CYP3A inhibition. In a previous study, coadministration of oxycodone and telithromycin, an inhibitor of CYP3A and CYP2D6, shifted the metabolism of oxycodone towards the CYP2D6-mediated route increasing oxymorphone concentrations [27]. After lopinavir/ritonavir, the geometric mean ratio for oxymorphone AUC was increased by 2.7-fold. Earlier studies have reported CYP2D6 inhibition during lopinavir/ritonavir [17,24].

The inhibition was not strong enough to totally hinder the compensation via CYP2D6-mediated route after CYP3A inhibition. The formation of oxymorphone has recently been shown to be dependent on CYP2D6 genotype [46]. In our study with only 12 healthy volunteers, there were 9 extensive, one intermediate, two ultrarapid but no poor metabolizers. In the present small-scale study these genotypes were not associated with changes in pharmacokinetic parameters of oxycodone or its metabolites.

The self-reported drug effect of oxycodone was significantly increased by both ritonavir and lopinavir/ritonavir. As well as oxycodone, also ritonavir was involved in the side-effects because approximately 25% of ritonavir users typically report nausea or vomiting [21]. Contrary to our previous studies, oxycodone reduced pain in the cold pressor test only in some subjects. As only a single dose of oxycodone was used in the current study in order to minimize the exposure of healthy volunteers to opioids, drug effect remained smaller compared to normal clinical setting. Moderate differences in drug concentrations do not necessarily affect the pharmacologic response because of the log-linear relationship between the drug concentration and effect. Furthermore, the power of this study was insufficient to show differences between ritonavir or lopinavir/ritonavir and placebo phases in pharmacodynamic parameters, because our primary interest was a change in the oxycodone AUC and the calculation of the sample size was based on that endpoint. The possibility of drug-drug interactions must be considered in the treatment of HIV-associated pain. In previous pharmacokinetic studies in healthy volunteers, for instance, the oral bioavailability of alfentanil was increased from 37% to 95% by steady-state ritonavir concentrations [18]. The acute administration of ritonavir reduced the clearance of intravenous fentanyl by 67% [16]. In HIV-negative subjects receiving buprenorphine/naloxone as opioid-replacement therapy, tipranavir/ritonavir reduced the formation of CYP3A4 dependent active metabolite norbuprenorphine, but the AUC of parent drug remained unchanged and no clinical opioid withdrawal symptoms were noted [47]. Contrary to the above mentioned studies, the concentrations of methadone have been decreased after ritonavir probably due to the induction of CYP2B6 [43]. Thus, the effect of ritonavir on important opioids can be opposite. Drug-drug interactions in HIVassociated pain management are not restricted only to opioids; concomitant administration of lopinavir/ritonavir caused a 46% reduction in the plasma concentrations of lamotrigine, which is an antiepileptic recommended as second or third line drug for the treatment of neuropathic pain [6]. The mechanism behind this interaction might be the induction in lamotrigine glucuronidation [48]. Theoretically, the induction of glucuronidation could have an influence on the metabolism of oxycodone and its metabolites, but these metabolic routes are of minor importance [49] and they were not investigated in this study. Additionally, it is unlikely, that short duration of ritonavir would have induced the glucuronidation of noroxycodone and oxymorphone significantly.

In conclusion, administration of lopinavir/ritonavir and ritonavir greatly increases plasma concentrations of oxycodone. Clinically, attention should be drawn to the concomitant use of oxycodone and ritonavir or lopinavir/ritonavir and oxycodone. Further studies are needed to extend the results to HIV or AIDS pain patients suffering from chronic pain.

The authors declare no conflicts of interest. infection. It has been estimated that in the developed world, depending on the stage of disease and study settings, 30-80% of people living with HIV/AIDS are affected with pain [1,2]. Pain experienced by HIV patients can be acute or chronic, and it is usually multifactorial in origin. HIV-associated pain can be due to e.g. HIV itself, antiretroviral treatment, opportunistic infections, HIV-associated cancers, rheumatic conditions or peripheral polyneuropathy [3][4][5]. Unfortunately, these painful conditions are often underrecognised and -treated.

Oxycodone is a semi-synthetic opioid agonist, which is effective in treating acute, cancer-related and chronic pain [6,7]. It is metabolized mainly via oxidative, but also via reductive pathways with only 10-14% being excreted in unchanged or conjugated forms in urine [8]. Oxycodone is principally a cytochrome P450 3A4/5 substrate, which makes it prone to the interactions with CYP3A inducers and inhibitors [9]. The main oxidative pathway includes N-demethylation to noroxycodone by CYP3A4/5 and further to noroxymorphone by CYP2D6 [9]. Approximately 11% of oxycodone is O-demethylated to oxymorphone by CYP2D6, which is further transformed to noroxymorphone by CYP3A4/5 and CYP2D6 [9]. Animal studies have given controversial results about the role of P-glycoprotein in the transport of oxycodone via blood-brain barrier (BBB ) [10,11]. However, the higher concentrations of oxycodone in rat and sheep brain tissue compared to plasma concentrations suggest that the influx protein is much more active than P-glycoprotein, which acts as an efflux protein limiting the concentrations of oxycodone in brain [12][13][14].

HIV protease inhibitors, such as ritonavir and lopinavir inflict the HIV-specific aspartic protease enzyme on processing the gag and gag-pol polyprotein precursor, which leads to the production of immature, noninfectious viral particles [15]. Ritonavir is a potent CYP3A, CYP2D6 and P-glycoprotein inhibitor [16][17][18]. It has been described to induce CYP1A2, CYP2B6, CYP2C9 and CYP2C19 [19,20]. Difficult dosing regimens, the development of resistance and adverse effects have restricted the use of ritonavir as an independent part of HIV treatment [21]. Lopinavir/ritonavir is a combination preparation, where lopinavir is Lopinavir/ritonavir has become a well-tolerated, effective and preferred protease inhibitor in the treatment of HIV. It is a substrate and inhibitor of CYP3A4 and to a lesser extent CYP2D6, and an inducer of CYP1A2, 2B6, 2C9 and 2C19 [23,24] which makes it prone to cytochrome P450-mediated interactions.

In previous studies it has been shown, that CYP3A inhibitors voriconazole [25], itraconazole [26] and telithromycin [27] increased the concentrations of oxycodone and oxymorphone, but only minor changes have been observed in the pharmacological action of oxycodone following single doses in healthy volunteers. As HIV patients are often affected with moderate to severe pain, there is an obvious need for the concomitant use of antivirals and strong opioids, such as oxycodone. The aim of this study was to investigate the effects of ritonavir and lopinavir/ritonavir on the pharmacokinetics and effects of oxycodone in healthy volunteers.

METHODS

Participants

The study protocol was approved by the Ethics Committee of the Hospital District of Southwest Finland and by the Finnish National Agency for Medicines and was and was thereby reported to the EudraCT clinical trial register. In view of our previous studies, it was calculated, that 10 subjects would be required to detect a 30% difference in the area under the oxycodone concentration-time curves (AUC 0-∞ ) at a level of significance P = 0.05 and power of 80% [25][26][27]. To allow a drop-out rate of 20%, we decided to recruit 12 subjects. Twelve healthy volunteers, four women and eight men (age 18-26 years; weight 52-84 kg) were studied according to the revised Declaration of Helsinki. Informed consent was obtained from all subjects before study screening. All participants were ascertained to be healthy by means of medical history and clinical examination, and they were tested for routine laboratory tests. Urine screens for drugs with abuse risk were negative and a 12-lead electrocardiogram (ECG) within normal limits. Pregnancy tests for women were negative. None of the participants were receiving any continuous medication including hormonal contraception and all were non-smokers. Women in need of contraception were instructed to use nonhormonal methods during the study. Finnish translation of Abuse Questions [28] was used to assess risk for drug abuse. The consumption of any product with potential CYP3A effect such as herbal remedies or grapefruit juice was forbidden for 4 weeks and all other drugs for 2 weeks before and during the study. Consumption of coffee, tee, alcohol and cola drinks were not allowed in the study facility during the study days. Genotyping for CYP2D6 was conducted by a two-step multiplex primer extension method [29]. This method enables the detection of 11 most relevant polymorphic positions, the definition of whole-gene deletion, duplication and the allele combination in case of gene duplication. The volunteers fasted for 8 h before oxycodone hydrochloride administration and received a standard meal 4 and 8 h after dosing. Drinking was allowed only during the meals. Adherence to the pre-treatment medication schedule was ensured with mobile phone text messages, which the volunteers sent to the investigator after taking each dose of pre-treatment taken outside the study facility.

Study design and drug dosing

Sampling and determination of drug concentrations

In the study facility, a cubital vein was cannulated for blood sampling. The samples (10 ml each) were collected into EDTA containing tubes before and 1, 1.5, 2, 3, 4, 5, 6, 8, 10, 12, 24, 48 h after the oxycodone hydrochloride dose. Plasma was separated within 30 min and stored at -70°C until analysis.

Plasma concentrations of oxycodone, noroxycodone, oxymorphone and noroxymorphone were analyzed with the liquid chromatography-tandem mass spectrometric (LC-MS/MS) method as described earlier [30].

The lower limits of quantification (LLQ) were 0.1 ng/mL for oxycodone and oxymorphone, and 0.25 ng/mL for noroxycodone and noroxymorphone. The interday coefficient of variation was less than 6% for parent oxycodone and less than 13% for the metabolites at relevant plasma concentrations. The concentrations of ritonavir and lopinavir just before and 2 h after the administration of the dose of pretreatment on the day 3 were determined with a LC-MS/MS method [31]. Saquinavir was used as internal standard. The LLQ was 0.1 µg/ml for ritonavir and 0.4 µg/ml for lopinavir. The interday CVs were < 5.4% and < 7.0% for ritonavir and lopinavir, respectively, at relevant concentrations (n=3).

Pharmacokinetic analysis

The pharmacokinetics of oxycodone and its metabolites were determined by standard noncompartmental methods using a pharmacokinetic program WinNonlin (version 4.1, Pharsight Corporation, Mountain View, CA, USA). The peak plasma concentrations (C max ) and the corresponding C max times (t max ) were observed directly from the data. The elimination rate constant (k e ) was determined by log-linear regression. The elimination half-life t ½ was calculated from ln2/k e . The areas under the oxycodone, noroxycodone, oxymorphone and noroxymorphone concentration-time curves (AUC 0-∞ ) were calculated by trapezoidal rule with extrapolation to infinity. Whenever k e could not be estimated because of low plasma concentrations of the metabolite, AUC 0-48 was used instead of AUC 0-∞ . The metabolite-to-parent drug AUC ratios (AUC m /AUC p ) were calculated to compare the relative abundance of each metabolite.

Analysis of pharmacological effects

Behavioral effects

The behavioural measures were performed before and at 1, 2, 3, 4, 5, 6, 8, 10 and 12 h after oxycodone as described earlier [32]. Subjective assessment of drowsiness, deterioration of performance, self-reported drugeffect, pleasantness and nausea or vomiting was quantified using visual analog scales (VAS) and scored 0-100. Attributes assessed included alert/drowsy, good/poor performance, no/strong drug effect, unpleasant/pleasant feeling and no/extreme nausea or vomiting [33]. Maddox wing test was used to estimate the central coordination of extraocular muscles [34]; digit symbol substitution test (DSST) to estimate the central processing of sensory information [35]

Experimental pain

The analgesic effect was studied in an experimental cold pressor test, which is sensitive to opioid effects [37]. The subjects placed their left hand up to the wrist into the ice water bath (temperature 0-2ºC) for one minute and reported the first sensation of pain (cold pain threshold, CPT), which was measured in seconds with a stopwatch. The intensity of cold pain at 60 s (CPI) was assessed using numerical rating scale (NRS 0-100). The area under the drug effect-time curve (AUEC 0-12 ) from 0 to 12 hours was calculated with linear trapezoidal rule based on the observations by the use of the WinNonlin (version 4.1, Pharsight Corporation, Mountain View, CA, USA) software for all effect variables.

Statistics

The results are expressed as mean ± SD, except for t max , where medians with minimum and maximum values are reported. In figures, for clarity, mean ± SEM are given. Descriptive and comparative statistics were calculated with use of SYSTAT for Windows (version 10.2; Systat software, Richmond, CA, USA). The pharmacokinetic and pharmacodynamic variables were compared with the use of analysis of variance for repeated measures and a posteriori testing was performed with Tukey's test. Pharmacokinetic data was logarithmically transformed before statistical comparisons. Analysis of variance was used to determine the contributions of treatment, CYP2D6-genotype, gender, phase and sequence on overall variance. The t max was analyzed by Friedman's test and Wilcoxon signed rank test was used for pair wise comparisons. P was considered significant at the level of <0.05. We also calculated geometric mean ratios with 90% confidence intervals (CI) for the pharmacokinetic parameters. Bioequivalence (i.e., the lack of an interaction) was concluded, if the 90% CI of the geometric mean ratios (coadministration/oxycodone alone) were within the acceptance limit of 0.8 to 1.25.

Pharmacokinetic results

The pharmacokinetic results are presented in Figure 1 and Table 1. During the ritonavir and lopinavir phases, all of the calculated pharmacokinetic parameters for oxycodone were outside the bioequivalence acceptance limits (Table 1). Compared with placebo, ritonavir and lopinavir/ritonavir increased the oxycodone AUC 0-∞ (geometric mean ratio) by 3.0-fold (90% CI 2.6, 3.4; P<0.001) and 2.6-fold (90% CI 2.3, 2.9; P<0.001), respectively. Ritonavir increased the geometric mean ratio of oxycodone C max by 1.7-fold (90% CI 1.5, 2.0; P<0.001) and prolonged its elimination half-life from 3.6 ± 0.6 h to 5.6 ± 0.9 h (P<0.001).

Lopinavir/ritonavir increased the geometric mean ratio of oxycodone C max by 1.4-fold (90% CI 1.2, 1.7; P<0.05) and prolonged its elimination half-life to 5.7 ± 0.9 hours (P<0.001). Gender had no significant influence on the pharmacokinetic parameters of oxycodone.

Both ritonavir and lopinavir/ritonavir decreased the geometric mean ratio for noroxycodone AUC 0-48 to 0.49 (90%CI 0.43, 0.58, P<0.001) and 0.44 (90%CI 0.38, 0.50; P<0.001), respectively. Ritonavir alone had no effect on oxymorphone but lopinavir/ritonavir increased geometric mean ratio for oxymorphone AUC 0-48 by 2.7-fold (90%CI 2.0, 3.8; P<0.05). The geometric mean ratio for noroxymorphone AUC 0-48 was 0.19 (90%CI 0.15, 0.24; P<0.01) after ritonavir and 0.24 (90%CI 0.16, 0.35; P<0.01) after lopinavir/ritonavir, respectively. Four subjects during ritonavir phase and three subjects during lopinavir/ritonavir phase had no detectable concentrations of noroxymorphone during the study period 0-48 hours.

Nine volunteers were classified as extensive metabolizers (EM). Three of them were homozygous for CYP2D6*1 allele and the rest of the genotypes were CYP2D6*1/4 (n = 3), CYP2D6*1/5 (n = 2) and CYP2D6*1/41 (n=1). Furthermore, there were two subjects with CYP2D6*1/1 x 2 genotype classified as ultrarapid metabolizer (UM) and one CYP2D6*10/*41 classified as intermediate metabolizer (IM). The IMs After pre-treatment with ritonavir, plasma ritonavir concentrations (mean ± SD) measured ½ and 3 hours after the morning dose on day 3, were 3.2 ± 1.6 µg/ml and 8.7 ± 2.6 µg/ml. After lopinavir/ritonavir pretreatment, the corresponding plasma lopinavir concentrations were 9.0 ± 2.0 µg/ml and 11.3 ± 2.9 µg/ml and ritonavir concentrations 0.5 ± 0.3 µg/ml and 1.0 ± 0.5 µg/ml, respectively. All volunteers had measurable values of ritonavir and ritonavir/lopinavir before the morning dose during active phases indicating compliance to the dosing schedule.

Pharmacodynamic results

Results from behavioral and analgesic tests are shown in Fig. 2. The self-reported drug effect AUEC 0-12 increased significantly after ritonavir and lopinavir/ritonavir (P<0.001 and <0.05). Ritonavir increased also nausea or vomiting as measured by VAS (P<0.01), but no other statistically significant differences were observed in the pharmacological response to oxycodone.

The adverse effects reported are presented in Table 2. All three volunteers with vomiting were treated with 2 mg intravenous tropisetron 4, 8 or 12 h after oxycodone.

DISCUSSION

This study convincingly demonstrates that short-term administration of ritonavir and lopinavir/ritonavir strongly inhibits the CYP3A-mediated N-demethylation of oxycodone, as reflected by an approximately 2-3fold increase in oxycodone AUC 0-∞ and decrease in noroxycodone AUC 0-48 . When oxycodone was administered together with ritonavir or lopinavir/ritonavir, the mean concentration of oxycodone was still higher at 8 h than the C max during the control phase. These pharmacokinetic changes were accompanied by enhanced self-reported drug-effect after both antiviral treatments.

Ritonavir has been shown to be a strong inhibitor of CYP3A both in vitro [38,39] and in vivo [16,18,40,41]. In the present study ritonavir caused a 3-fold mean increase of oxycodone AUC 0-∞ , which is somewhat smaller than that observed after the azole antimycotic voriconazole (3.6-fold increase in oxycodone AUC 0-∞ ), but bigger than that observed for itrazonazole (2.4-fold increase) and telithromycin (1.8-fold increase) [25][26][27]. It can be speculated, although it seems doubtful, that the interaction between oxycodone and ritonavir would have been stronger, if a larger ritonavir dose would have been used as recommended by the manufacturer. However, because we wanted to minimize possible adverse effects in healthy volunteers, 300 mg b.i.d. was used and the treatment lasted only four days. This could lead to bias, as the steady-state was not reached [21]. Ritonavir has been reported to cause an enzyme induction after approximately 2 weeks administration, which can diminish the inhibitory effects [42,43].

Interestingly, lopinavir/ritonavir resulted in an almost equal alteration in oxycodone pharmacokinetics as compared to ritonavir. The clinically used maintenance dose of ritonavir is 600 mg b.i.d., which is recommended to be attained during 2 weeks to avoid adverse effects. During the 4-day pre-treatment our volunteers were given 300 mg of ritonavir b.i.d., which is three times higher than the dose of ritonavir during lopinavir/ritonavir phase. Still, the pharmacokinetics of oxycodone was very similar whether it was given together with ritonavir or lopinavir/ritonavir. A plausible explanation for the observed pharmacokinetic changes is the additional inhibitory effect by lopinavir. Lopinavir is a less potent inhibitor of CYP3A in vitro than ritonavir, but it might nonetheless contribute to net CYP3A inhibition in vivo during the treatment with 12 the combination of lopinavir and ritonavir. The dose-response curve for CYP3A inhibition by ritonavir can be so flat in vivo that despite a 3-fold dose reduction, it causes a maximal inhibition of CYP3A when combined with a 4-fold higher dose of lopinavir [44].

Ritonavir inhibits CYP2D6 in vitro, but to a lesser extent than it inhibits CYP3A [44,45]. In the present study, oxymorphone concentrations were increased during both antiviral phases indicating the increased significance of compensatory oxycodone metabolic route via CYP2D6 during CYP3A inhibition. In a previous study, coadministration of oxycodone and telithromycin, an inhibitor of CYP3A and CYP2D6, shifted the metabolism of oxycodone towards the CYP2D6-mediated route increasing oxymorphone concentrations [27]. After lopinavir/ritonavir, the geometric mean ratio for oxymorphone AUC was increased by 2.7-fold. Earlier studies have reported CYP2D6 inhibition during lopinavir/ritonavir [17,24].

The inhibition was not strong enough to totally hinder the compensation via CYP2D6-mediated route after CYP3A inhibition. The formation of oxymorphone has recently been shown to be dependent on CYP2D6 genotype [46]. In our study with only 12 healthy volunteers, there were 9 extensive, one intermediate, two ultrarapid but no poor metabolizers. In the present small-scale study these genotypes were not associated with changes in pharmacokinetic parameters of oxycodone or its metabolites.

The self-reported drug effect of oxycodone was significantly increased by both ritonavir and lopinavir/ritonavir. As well as oxycodone, also ritonavir was involved in the side-effects because approximately 25% of ritonavir users typically report nausea or vomiting [21]. Contrary to our previous studies, oxycodone reduced pain in the cold pressor test only in some subjects. As only a single dose of oxycodone was used in the current study in order to minimize the exposure of healthy volunteers to opioids, drug effect remained smaller compared to normal clinical setting. Moderate differences in drug concentrations do not necessarily affect the pharmacologic response because of the log-linear relationship between the drug concentration and effect. Furthermore, the power of this study was insufficient to show differences between ritonavir or lopinavir/ritonavir and placebo phases in pharmacodynamic parameters, because our primary interest was a change in the oxycodone AUC and the calculation of the sample size was based on that endpoint. The possibility of drug-drug interactions must be considered in the treatment of HIV-associated pain. In previous pharmacokinetic studies in healthy volunteers, for instance, the oral bioavailability of alfentanil was increased from 37% to 95% by steady-state ritonavir concentrations [18]. The acute administration of ritonavir reduced the clearance of intravenous fentanyl by 67% [16]. In HIV-negative subjects receiving buprenorphine/naloxone as opioid-replacement therapy, tipranavir/ritonavir reduced the formation of CYP3A4 dependent active metabolite norbuprenorphine, but the AUC of parent drug remained unchanged and no clinical opioid withdrawal symptoms were noted [47]. Contrary to the above mentioned studies, the concentrations of methadone have been decreased after ritonavir probably due to the induction of CYP2B6 [43]. Thus, the effect of ritonavir on important opioids can be opposite. Drug-drug interactions in HIVassociated pain management are not restricted only to opioids; concomitant administration of lopinavir/ritonavir caused a 46% reduction in the plasma concentrations of lamotrigine, which is an antiepileptic recommended as second or third line drug for the treatment of neuropathic pain [6]. The mechanism behind this interaction might be the induction in lamotrigine glucuronidation [48]. Theoretically, the induction of glucuronidation could have an influence on the metabolism of oxycodone and its metabolites, but these metabolic routes are of minor importance [49] and they were not investigated in this study. Additionally, it is unlikely, that short duration of ritonavir would have induced the glucuronidation of noroxycodone and oxymorphone significantly.

In conclusion, administration of lopinavir/ritonavir and ritonavir greatly increases plasma concentrations of oxycodone. Clinically, attention should be drawn to the concomitant use of oxycodone and ritonavir or lopinavir/ritonavir and oxycodone. Further studies are needed to extend the results to HIV or AIDS pain patients suffering from chronic pain.

The authors declare no conflicts of interest. The results are given as mean ± SD and geometric mean ratio (90% CI), except for t max , which is given as mean and range. Significantly different *(P<0.05), **(P<0.01) or ***(P<0.001) from the control phase. C max = maximum concentration, t max = time to maximum concentration, AUC 0-∞ = area under plasma concentration-time curve extrapolated to infinity, AUC 0-48 = area under plasma concentration-time curve from zero to 48 hours, t ½ = elimination half-life.

  randomized, crossover study design with three phases at intervals of four weeks. During each phase, the volunteers were given 4 capsules or tablets b.i.d. at 7 a.m. and 7 p.m. for 4 days. The capsules and tablets were packed into sealed coded envelopes which contained either:1. Two lopinavir/ritonavir 200 mg/50 mg tablets (Kaletra ® , Abbott Laboratories Ltd, Queenborough, UK) and two placebo capsules 2. Three ritonavir 100 mg capsules (Norvir ® , Abbott Laboratories Ltd, Queenborough, UK) and one placebo capsule 3. Four placebo capsules.An oral dose of 10 mg oxycodone hydrochloride (Oxynorm ® 10 mg capsule, Mundipharma, Bard Pharmaceuticals Ltd, Cambridge, UK) was administered on day 3 at 8 a.m. with 150 ml warm water. The participants arrived at the study facility one hour before oxycodone administration and were under supervision 12 hours after the dosing.

9 RESULTS

 9 All subjects completed the study.

  Nine volunteers were classified as extensive metabolizers (EM). Three of them were homozygous for CYP2D6*1 allele and the rest of the genotypes were CYP2D6*1/4 (n = 3), CYP2D6*1/5 (n = 2) and CYP2D6*1/41 (n=1). Furthermore, there were two subjects with CYP2D6*1/1 x 2 genotype classified as ultrarapid metabolizer (UM) and one CYP2D6*10/*41 classified as intermediate metabolizer (IM). The IMs lower oxymorphone AUC values than most of the extensive metabolizers during all phases (data not shown).

INTRODUCTION

  supported by Turku University Hospital research fund EVO 13821 and Turku University Foundation, Turku, Finland; Sigrid Jusélius Foundation and Instrumentarium Research Foundation, Helsinki, Pain is one of the most frequently reported symptoms associated with human immunodeficiency virus (HIV)

  low-dose ritonavir to enhance its low bioavailability by favourable CYP3A inhibition[22].

  randomized, crossover study design with three phases at intervals of four weeks. During each phase, the volunteers were given 4 capsules or tablets b.i.d. at 7 a.m. and 7 p.m. for 4 days. The capsules and tablets were packed into sealed coded envelopes which contained either:1. Two lopinavir/ritonavir 200 mg/50 mg tablets (Kaletra ® , Abbott Laboratories Ltd, Queenborough, UK) and two placebo capsules 2. Three ritonavir 100 mg capsules (Norvir ® , Abbott Laboratories Ltd, Queenborough, UK) and one placebo capsule 3. Four placebo capsules.An oral dose of 10 mg oxycodone hydrochloride (Oxynorm ® 10 mg capsule, Mundipharma, Bard Pharmaceuticals Ltd, Cambridge, UK) was administered on day 3 at 8 a.m. with 150 ml warm water. The participants arrived at the study facility one hour before oxycodone administration and were under supervision 12 hours after the dosing.

9 RESULTS

 9 All subjects completed the study.

  lower oxymorphone AUC values than most of the extensive metabolizers during all phases (data not shown).

  supported by Turku University Hospital research fund EVO 13821 and Turku University Foundation, Turku, Finland; Sigrid Jusélius Foundation and Instrumentarium Research Foundation, Helsinki,

Fig. 1 .Fig. 2 .

 12 Fig. 1. Plasma concentrations (mean ± SEM) of oxycodone and its metabolites in 12 healthy volunteers after administration of placebo (control), ritonavir (RIT, 300 mg b.i.d.) or lopinavir/ritonavir (LPV/r, 400/100 mg b.i.d.) for four days. Oxycodone hydrochloride (10 mg per os) was given on day 3 at 8 a.m. with 150 ml of warm water. For clarity, SEMs were used instead of SDs. SEMs can be converted into SDs by multiplying it with square root of 12 (3.46). Time = 0 refers to the time of oxycodone administration.

  and Cogan's pupillometry to measure pupil diameter[36]. All reported and observed adverse effects were recorded.

  and Cogan's pupillometry to measure pupil diameter[36]. All reported and observed adverse effects were recorded.

Table 1 .

 1 Pharmacokinetic parameters of oxycodone and its primary and secondary oxidative metabolites after oral administration of 10 mg of oxycodone hydrochloride following pre-treatment with placebo (control), oral ritonavir or lopinavir/ritonavir in 12 healthy volunteers.

	Parameter	Control phase	Ritonavir phase	Lopinavir/ritonavir phase
			[geometric mean ratio (90% CI)]	[geometric mean ratio (90% CI)]
	Oxycodone
	C max (ng/ml)	20.2 ± 6.8	34.1 ± 7.3*** [1.74 (1.55-1.95] 27.7 ± 7.2* [1.38 (1.17-1.66)]
	t max (h)	1.5 (0.5-1.5)	1.5 (0.5-5.0)	2.5 (0.5-5.0)*
	AUC 0-∞ (µg•min/ml)	6.8 ± 2.3	19.6 ± 4.6*** [2.95 (2.57-3.39)] 17.3 ± 4.3*** [2.57 (2.34-2.88)]
	t ½ (h)	3.6 ± 0.6	5.6 ± 0.9*** [1.55 (1.45-1.62)]	5.7 ± 0.9*** [1.58 (1.48-1.66)]
	Noroxycodone
	AUC 0-48 (µg•min/ml)	7.6 ± 2.1	3.8 ± 1.4*** [0.49 (0.43-0.58)]	3.4 ± 1.5*** [0.44 (0.38-0.50)]
	Oxymorphone
	AUC 0-48 (µg•min/ml)	2.19 ± 1.77	2.8 ±2.42 [1.07 (0.60-1.90)]	5.0 ± 2.2* [2.69 (1.95-3.80)]
	Noroxymorphone
	AUC 0-48 (µg•min/ml)	1.65 ± 0.63	0.27 ± 0.26** [0.19 (0.15-0.24)] 0.03 ± 0.03** [0.24 (0.16-0.35)]

Table 2 .

 2 Adverse effects in 12 healthy volunteers after placebo, ritonavir and lopinavir/ritonavir pretreatment and during test days, after placebo, ritonavir and lopinavir/ritonavir and oral oxycodone hydrochloride 10 mg.

			Pre-treatment			Test days
	Adverse effect Placebo	Ritonavir	Lopinavir/	Placebo	Ritonavir	Lopinavir/
				ritonavir			ritonavir
	Dizziness	0	0	0	5	5	7
	Itching	0	0	0	4	4	4
	Tiredness	0	0	0	2	0	0
	Diarrhoea	0	2	3	0	0	0
	Abdominal	0	3	3	0	0	0
	Irritation
	Nausea	0	0	3	4	4	2
	Vomiting	0	0	0	2	2	1
	(n=number of subjects)

European Journal of Clinical Pharmacology

Acknowledgements:

We want to thank Mrs. Elina Kahra, Medical laboratory technologist, and Mrs. Mia Suppanen-Olkkola, RN, for their skilful technical assistance; Dr. Juha Grönlund, M.D., for his practical help during the data collection and Mrs. Kaisa J. Kurkinen, M.Sc., for the determination of ritonavir and lopinavir concentrations.

Acknowledgements:

We want to thank Mrs. Elina Kahra, Medical laboratory technologist, and Mrs. Mia Suppanen-Olkkola, RN, for their skilful technical assistance; Dr. Juha Grönlund, M.D., for his practical help during the data collection and Mrs. Kaisa J. Kurkinen, M.Sc., for the determination of ritonavir and lopinavir concentrations.