Demographic characteristics and quality of life of patients with unexplained complaints: a descriptive study in general practice

Objective About 13% of GPs’ consultations involve unexplained complaints (UCs). These complaints can progress to chronic conditions like medically unexplained symptoms, chronic functional symptoms or somatoform disorders. Little is known about the demographic characteristics and quality of life of patients with early stage UCs. Our study objective was to describe these characteristics. Additionally we compared them with other patient groups to serve as a frame of reference.
Introduction
A patient’s complaints are said to be unexplained if the general practitioner (GP) cannot decide on a specific diagnosis after adequate history taking, physical examination and careful consideration of the patient’s psychosocial context [1]. On average, 13% of consultations involve complaints considered unexplained by Dutch GPs [2]. These unexplained complaints often concern fatigue, abdominal and musculoskeletal complaints.
It is often assumed that unexplained complaints are mild and self-limiting, because patients do often not revisit their GP for them after a first consultation [3, 4]. However, an unexplained complaint can be a first sign of somatic or psychosocial pathology or a precursor of more chronic unexplained complaints such as chronic medically unexplained symptoms (MUS), chronic functional symptoms or somatoform disorders [5].
Patients with chronic unexplained conditions often have high levels of medication and other healthcare use and frequently show significant psychological distress [6]. Patients with multiple medically unexplained symptoms (MMUS) have impaired quality of life [7]. Furthermore, patients often do not feel taken seriously, do not feel helped and feel treated as malingerers. They, therefore, tend to lose trust in their GPs [8]. In brief, the long-term consequences of many unexplained complaints are unfavourable.
GPs too consider unexplained complaints problematic. One study found that in consultations with such patients GPs feel frustrated and helpless though devoted to help [9].
Research on unexplained complaints is increasing. So far, most research has focused on patients with chronic consequences of unexplained complaints. This research varies from discussions on nomenclature and taxonomy [10–12], to research on the characterization of these patient groups [13, 14] and possible treatment options [15–17]. Little is known about patients with unexplained complaints in their early stages. We performed this study to describe the demographic characteristics and quality of life of patients with early stage unexplained complaints. To provide some frame of reference, we compared these descriptive measures for the unexplained complaints to those of an unselected group of patients visiting their GP, and a group of depressed patients. For practitioners, better delineation, in terms of quality of life, of a group of patients that frequently seeks their help may sensitize them to consider more structured monitoring in order to achieve a firmer diagnosis be it somatic, psychological, or psychosocial.
Methods
GPs and patients
GPs were recruited from the southern and the western part of the Netherlands. GPs were given the Dutch College of General Practitioners’ definition of unexplained complaints: those complaints that remain of unclear origin for the GP after adequate history taking, physical examination and careful consideration of the patient’s psychosocial context [1]. This definition supposes that complaints are labelled “unexplained” early on in the clinical episode before elaborate investigations were performed.
Between February 2002 and December 2003, participating GPs included adult patients, presenting with a complaint that the GP designated as “unexplained” at the end of the first consultation. Patients who had presented with the same complaint in the previous 6 months were not included. Patients were eligible if they presented with fatigue, abdominal complaints, musculoskeletal complaints, weight changes or itch as their main complaint.
Individual GPs decided whether a complaint was “unexplained”. There was no standardization of this process other than that implied by the Dutch College of General Practitioners’ definition. GPs invited patients to participate in the study at the end of the consultation.
Informed consent procedure and medical ethics committees
All participating patients gave written informed consent after having read information provided by the GP directly after the consultation. The medical ethics committees of the University of Amsterdam and Maastricht University approved the study protocol.
Data collection: study population
The data for this study were collected as part of baseline measurements for a randomized diagnostic trial on unexplained complaints in general practice [18]. After the entry consultation, patients filled out a questionnaire on demographic characteristics. The RAND 36-item Health Survey (RAND-36) was used to measure generic health-related quality of life. The RAND-36 [19] is a Dutch version of the Medical Outcome Study 36-item Short Form Health Survey (SF-36) [20] and is composed of 36 questions and standardized response choices, organized into eight multi-item scales (domains). These eight domains are: physical functioning (PF), role limitations due to physical health problems (RP), social functioning (SF), general mental health (MH), role limitations due to emotional problems (RE), vitality (VT), bodily pain (BP) and general health perception (GH). Raw RAND-36 scores on the eight domains are linearly converted to 0–100 scales with higher scores indicating better quality of life.
Data collection: reference populations
Reference population for comparison of demographic characteristics
The demographic characteristics of a Dutch general practice population were taken from the second Dutch National Survey of General Practice, which is a large representative population-based survey that takes place every 10 years. Methods of this study have been published elsewhere [21]. Briefly, this national survey contains data about health and healthcare-related behaviour of 375,899 persons, registered in 104 practices with 195 GPs in 2001.
Reference populations for comparison of quality of life
The results section of this manuscript shows that the quality of life of patients with early stage unexplained complaints is poor. Our initial objective was purely descriptive. However, when such low quality of life figures were found, we felt the need to compare these to other patient groups. First, we wondered whether maybe all patients consulting their GP would have such low quality of life figures and therefore compared the unexplained complaints group with a Dutch general practice population. We also thought that maybe our patient population included a lot of depressed patients which could explain the poor quality of life and therefore also compared our quality of life figures with those of a population of depressed patients.
Figures of the Dutch general practice population came from a study on functional status, health problems, age and comorbidity in primary care patients [22]. In this study 60 GPs from 43 general practices handed out a written questionnaire to 100 patients of 18 years and older, consecutively visiting their practice. In total 4,024 patients responded.
Figures of the depressed patients came from the Netherlands Mental Health Survey and Incidence Study (NEMESIS). This NEMESIS survey, was based on a random sample drawn from the Dutch general adult population aged 18–64 (n = 7,076) of whom 204 had been diagnosed with a major depression within the past month [23].
Statistical analysis
First, we compared the demographic characteristics of the five unexplained complaint categories to each other. Those of the total UC study population were compared to those of a Dutch general practice population. Differences were tested using the Fisher’s exact test.
Second, mean domain scores on the RAND-36 were compared for each of the five unexplained complaint groups using linear regression with the “fatigue” complaint group as the reference category. We calculated robust variance estimates (Huber-White sandwich estimator) [24] to allow for potential dependence of quality of life scores within a single GP.
Lastly, we compared mean domain scores of the total UC study population to those of a Dutch general practice population sample and to a random sample of depressed patients. Differences were statistically tested with t-tests in which equal variances were not assumed. Analyses were performed using STATA, version 9.2.
Results
General characteristics of the study group
Of the 91 GPs who intended to participate, 18 dropped out before including any patients. They predominantly reported lack of time as the reason for drop out. Ten GPs did not include any patients during the inclusion period, although they did not formally drop out. Thus, 63 GPs (69%) included 513 patients with unexplained complaints (range: 1–36 patients per GP). Questionnaires from 466 (91%) patients were available for analysis. Forty-seven patients stopped their participation to the study or did not fill out this particular questionnaire.
Demographic characteristics of the study group
The demographic characteristics are summarized in Table 1, for the total UC study population, per complaint group and for the Dutch general practice population. The mean age of the total UC study population was 44 years (interquartile range 31–55) and 74% were women. Most patients had completed secondary or higher level education (91%) and were married or living together with a partner (71.5%).
Table 1Demographic characteristics of the total UC study population, per complaint group and of a Dutch general practice populationDemographic characteristicCategoriesTotal UC study population % (n = 466)Fatigue % (n = 295)Abdominal complaints % (n = 60)Musculoskeletal complaints % (n = 69)Other complaint groups % (n = 42)Dutch GP population % (n = 3,85,461)SexMale26.426.12524.633.349.5Female73.6a73.97575.466.750.5AgeMean (years)44.241.842.352.2 b50.3Unknown0–194.96.16.71.1023.420–3937.142451330.930.440–6445.9a41.74066.7503365+1210.28.318.819.113.2Educational levelNone1004.3 b2.416Elementary97.53.317.4 b14.318.9Secondary68.9a68.18059.5b64.348.1Higher22.1a24.416.718.81917Marital stateSingle28.529.82030.428.6UnknownMarried/cohabiting71.570.28069.571.4UnknownInsurance TypePublic65.265.868.369.65067.4Private34.834.231.730.45032.6NationalityDutch95.396.396.791.39386.2Not Dutch4.73.73.38.7713.8Region of the NetherlandsWest56.457.646.762.352.4UnknownSouth43.642.453.337.747.6Unknowna P < 0.05 (Fisher’s exact test, reference group is GP population)b P < 0.05 (Fisher’s exact test, reference groups are fatigue and abdominal complaints)
The main reason for encounter was fatigue (63.3%) and only few patients with weight changes or itch were included. In all further analyses, these latter two groups were combined into an “other complaints” group.
Patients with musculoskeletal complaints were significantly older and less educated than patients with fatigue or abdominal complaints (P < 0.05).
Compared to patients from the Dutch general practice population (second Dutch National Survey of General Practice), unexplained complaint patients were more likely to be women (74% vs. 51%), older (46% vs. 33% in the age group 40–64) and more highly educated (91% vs. 65% secondary or higher level education). These differences were statistically significant (P < 0.05).
Quality of life of the total UC study population and per unexplained complaint group
The overall quality of life for the total UC study population was poor (Table 2). All domains showed a mean score of less than 70 (on a scale from 0 to 100), except Physical Functioning (mean 73.4). The lowest score was in the Role Functioning Physical domain (mean 37.2). Also the Vitality domain scored low (mean 40.5) both in the total UC study population and in the separate complaint groups.
Table 2Quality of life of the total UC study population and per complaint groupAllFatigueAbdominalMusculoskeletalOtherN466295606942Domains RAND-36MeanMeanMeanΔ (95% CI)MeanΔ (95% CI)MeanΔ (95% CI)Physical functioning73.473.381.38.1 (2.2 to 14.0)a62.2−8.0 (−14.8 to −1.3)a76.33.0 (−4.3 to 10.4)Social functioning66.562.570.88.3 (−0.2 to 16.9)76.413.9 (6.9 to 21.0)a729.5 (−0.7to 19.7)Role functioning physical37.231.556.324.7 (11.9 to 37.5)a397.5 (−3.5 to 18.5)4715.4 (−6 to 31.4)Role functioning emotional60.254.174.420.3 (9.0 to 31.6)a74.220.1 (6.6 to 33.6)a59.35.2 (−10.4 to 20.8)Mental health63.861.869.47.6 (2.3 to 12.9)a67.96.1 (1.1 to 11.1)a62.91.0 (−6.8 to 8.8)Vitality40.533.653.720.1 (15.1 to 25.1)a54.120.5 (13.8 to 27.2)a48.514.9 (8.2 to 21.5)aBodily pain67.772.761.7−10.0 (−15.9 to −6)a55.5−17.2 (−25.2 to −9.2)a61.3−11.4 (−21.5 to −1.2)aGeneral health55.755.261.15.9 (3.4 to 8.4)a53.8−1.4 (−3.6 to 0.8)54.3−0.9 (−5 to 3.3)Differences (Δ) indicate differences with the fatigue subgroup, where the mean of the fatigue subgroup was subtracted from the other subgroup’s meanConfidence intervals (95% CI) were calculated using the Huber-White sandwich variance estimator which accounts for within physician correlation (for details, see main text)aP < 0.05 (linear regression, reference group is fatigue)
Except for the Physical Functioning and Bodily Pain scales, patients with unexplained fatigue significantly scored the worst (P < 0.05) in every domain, especially on the Role Functioning Physical domain (mean 31.5). Patients with musculoskeletal complaints showed the significantly lowest score on Physical Functioning (mean 62.2) and Bodily Pain (mean 55.5) (P < 0.05).
Comparison of quality of life with other patient groups
Table 3 shows that the total UC study population scored lower on all domains than the Dutch general practice population. These differences were statistically significant (P < 0.05) for all domains except Bodily Pain.
Table 3Comparison of RAND-36 scores of different patient groupsDomains RAND-36Total UC study populationDutch GP populationDepressionAge18–8718–80+18–64N4664,024204MeanMeanΔ (95% CI)MeanΔ (95% CI)Physical functioning73.478.55.1 (2.9 to 7.3)a81.27.8 (3.9 to 11.7)aSocial functioning66.574.58 (5.4 to 10,6)a62.5−4 (−8.4 to 0.4)aRole functioning physical37.262.425.2 (21.5 to 28.9)a63.426.2 (19.3 to 33.1)aRole functioning emotional60.27514.8 (10.6 to 19.0)a42.6−17.6 (−24.6 to −10.6)aMental health63.869.65.8 (4.0 to 7.6)a46.6−17.2 (−20.4 to −14.0)aVitality40.558.518 (16.1 to 19.9)a40.3−0.2 (−3.6 to 3.2)Bodily pain67.768.40.7 (−1.7 to 3.1)680.3 (−4.3 to 4.9)General health55.765.710 (8.9 to 11.1)a55.70 (−3.2 to 3.2)Differences (Δ) indicate differences with the total UC study population, where the mean of the total UC study population was subtracted from the other patient group’s mean95% CI: 95% confidence intervalaP < 0.05 (t-test with equal variances not assumed, reference group is total UC study population)
Patients from the depression group scored significantly lower on the Role Functioning Emotional and Mental Health domains. On all the other domains the total UC study population scored equally high or significantly lower (Physical Functioning and Role Functioning Physical, P < 0.05) than patients from the depression group. In other words, unexplained complaint patients scored lowest on predominantly physically oriented domains, whereas depressed patients scored lowest on predominantly mentally/emotionally oriented domains.
Discussion
Our findings indicate that patients with early stage unexplained complaints are mainly women in their forties, with secondary or higher education levels and with an overall remarkably poor quality of life. Their quality of life in all but one domain of the RAND-36 is significantly worse than that of patients from a general practice population, even taking into account that such a population also includes (around 13% of) unexplained complaint patients. Patients with unexplained complaints predominantly score badly on physically oriented domains, compared to depressed patients, who predominantly score badly on mental/emotional oriented domains. For the remaining domains they score on a comparably low level. Therefore, practitioners may consider to pay attention to quality of life aspects of patients with early stage unexplained complaints, even though this may not always lead to a firmer diagnosis or instant improvement in treatment options for most patients. A more intense monitoring of these patients could, however, be advised.
The total UC study population scored very low on the Vitality domain of the RAND-36 (mean 40.5). This domain consists of questions regarding spirit and energy. The Vitality domain of the SF/RAND-36 is known to be correlated with subjective feelings of fatigue [25] and we therefore interpret this finding as a logical consequence of the high prevalence of patients with fatigue in the study population. Patients with unexplained complaints do not seem to have predominantly psychosocial problems. The domains Social Functioning and Mental Health were not the worst scoring domains in the study population (mean 66.5 and 63.8 respectively). The Role Functioning Physical domain scored much lower (mean 37.2), suggesting that patients with unexplained complaints feel that their physical complaints hinder them in their daily functioning. Of the five categories of unexplained complaints, patients with unexplained fatigue have the poorest quality of life.
A first potential limitation of our study can be that different definitions and conceptualizations of unexplained complaints limit the generalizability of our findings. In our study, unexplained complaints were labelled “unexplained” by the GPs in the first consultation after onset, before more elaborate diagnostics. This is in contrast to medically unexplained symptoms (MUS), which refer to symptoms that have been ruled out to have an explainable cause (diagnosis by exclusion). Our concept of early stage unexplained complaints does not allow one to draw firm inferences as to whether the lower quality of life levels can be attributed to the unexplained complaints or should be attributed to underlying, though not yet diagnosed conditions, or concurrent comorbidity.
Second, we cannot fully exclude the possibility that some degree of selection bias is present. Participating GPs may have selectively included older patients or those with poorer quality of life. However, in a non-inclusion study in the participating general practices, we searched the electronic medical files by means of text words for eligible but not included patients with unexplained complaints. This non-inclusion study did not show major sex and age differences between included and not-included patients. However, differences may exist on other characteristics.
A third limitation of our study is that no specific depression or anxiety questionnaire was used. At the start of the study, such a poor quality of life was not anticipated and, therefore, only a more general questionnaire was considered sufficient. The RAND-36 mentally/emotionally oriented domain figures and the differences we found when comparing the quality of life profile of unexplained complaint patients with depressed patients however, are not pointing towards the presence of depression or anxiety. Furthermore, the GPs did not consider the included patients to be depressed or suffering from an anxiety disorder, otherwise they would not have labelled the patient as having unexplained complaints by definition. It is not impossible however, that depression or anxiety disorders might play a role in the poor quality of life of patients with unexplained complaints. Maybe these diagnoses are established only by GPs over time, and do not become clear in (one of) the first consultations.
Finally, the patient groups used for comparison of quality of life are perhaps not totally comparable to our study population. For example, there are older patients included in our study population than in the depressed patient groups. Since age has its influence on quality of life this can have influenced the contrast. Also, a depression is a treatable condition, whereas early stage unexplained complaints are not treated yet. The better quality of life in this patient group on some domains can therefore be a treatment effect. We did, however, not intend to study a fully comparable contrast in this sense beforehand, it was a result driven comparison.
Although much research has been performed in patients with more chronic consequences of unexplained complaints, to our knowledge, no other study on demographic characteristics and quality of life of patients with early stage unexplained complaints in general practice has been published.
Patients with unexplained complaints appear to be mainly highly educated women in their forties. They report remarkably poor levels of quality of life. Future research should explore whether and how quality of life scores and other characteristics could help in early identification of patients at risk of developing chronicity. Until then practitioners should at least be aware that early stage unexplained complaints may not always be as mild as is frequently assumed. Early stage unexplained complaints may be associated with considerable suffering on a daily basis. Awareness of potential poor quality of life may help physicians realise that they are dealing with a patient, at least, in need of more intense monitoring but maybe also of more intense treatment approaches.

quality of life
unexplained complaints
descriptive study
fatigue
family practice


Low intense physical exercise in normobaric hypoxia leads to more weight loss in obese people than low intense physical exercise in normobaric sham hypoxia

Training in mild to moderate hypoxia (14–17% O2 in breathing air) and extended resting in moderate hypoxia (9–13% O2) have been shown to have effects in animals and humans on lipid and glucose metabolism, appetite loss, and, in part, on body weight. The causality for these effects is not yet known in detail, and the available data in humans from high-altitude and low-pressure chamber studies are scarce. New technical developments by German companies in the production of artificial climates with normobaric hypoxic conditions in larger rooms at reasonable energy costs allow now to perform hypoxia weight loss studies in obese humans with stable experimental conditions and protocols with a sham hypoxia control. Thirty-two obese people were recruited for a mild intense training study in normobaric hypoxia (15 vol.% O2) and normoxia/sham hypoxia (20.1 vol.% O2). Twenty of these [mean age 47.6 years, mean body mass index (BMI) 33.1, 16 m, 4 f) were willing to follow up on an 8-week, three times per week, 90-min low intense physical exercise in their individual fat burning mode, which has been determined by an exercise testing with spiro-ergometry upfront. The subjects were evenly randomized into a hypoxia and sham hypoxia group. The difference of the two groups in weight loss and changes in HBa1C values were analyzed before and after the training period. No nutritional diet was applied. Subjects in the hypoxia group in mean lost significantly more weight than in the sham hypoxia group (Δ1.14 kg vs Δ0.03 kg; p = 0.026). This resulted in a tendency to reduce the BMI more in the hypoxia group (p = 0.326). In the mean, there was no HbA1C exceeding normal values (mean 5.67 and 5.47%), and the HbA1C stayed basically unchanged after the 8-week training. Mild physical exercise three times per week for 90 min in normobaric hypoxia for 8 weeks led to significantly greater weight loss in obese persons than the exercise in sham hypoxia in this, to our knowledge, first sham hypoxia controlled study.
Introduction
High altitude exposure which cause hypoxaemia has various serious effects on the human body. Those effects are studied by physiologists since the nineteenth century, and science has shed light on a lot of physiological and pathophysiological body reactions [1].
Weight loss in high altitude under hypoxic climate conditions is one of those effects in which the causes remain still unclear, and it is not known if it is dose/altitude-dependent and affects everybody or only certain individuals [3–5].
The detection of the hypoxia-induced factor-alpha 1 (HIF-α1) and the chain reaction on mediators that this factor starts have prompted some speculations and investigations on various nutritional signals. One of the nutritional signals that seem to be triggered by HIF is the peptide hormone leptin [6–9]. This could lead to the direction that the human lipid metabolism is affected by hypoxia and that hypoxia causes signals to reduce food intake and also changes in fat mobilization and fat uptake to the body [10].
Because the societies in first and second world countries struggle with an increased body weight, pathological fat levels, and the increasing number of individuals with metabolic syndrome in their populations, the urge for various inexpensive treatment forms including a lifestyle change has risen. The idea is coming up whether training or stay at moderate altitude or simulated altitude could enhance weight loss and improvement of fat and sugar metabolism because of the known effects of hypoxia on weight loss. Several open trial field studies with low numbers of individuals have been carried out so far, but they do not have controls [11–13]. In the AMAS 2000 study, patients with metabolic syndrome have trained and stayed for 3 weeks at 1,700 m altitude or within a control group at sea level. In this randomized controlled study, significant improvements in fat metabolism and regarding weight loss have been found for the patients at altitude compared to patients at sea level [14].
The construction design of our facility with normobaric hypoxic climate rooms allows pretending hypoxic conditions without actually running the rooms in hypoxic climate (sham hypoxia) and to perform a single blind randomized controlled weight loss study in simulated altitude.
Our aim was to perform a physical exercise weight loss study in obese subjects under moderate hypoxia (15 vol.% O2) and to compare the effects on body weight and fat metabolism to a control group training on the same level in sham hypoxic conditions.
Materials and methods
Site Our facility for simulated altitude in the renovated Kurmittelhaus der Moderne in Bad Reichenhall in southeast Bavaria has three rooms (90, 37, and 25 m2) in which a hypoxic climate at normobaric conditions can be produced up to an simulated altitude of 6,000 m (approximately 7 vol.% O2; see Fig. 1).
Fig. 1The 90 m2 hypoxia training room with exercise devicesThe rooms are two well-equipped training facilities and one sleeping and rest room with a full bath. To reach a hypoxic climate, the air in the room is flushed with nitrogen, and the volume percent of oxygen is reduced to the determined level. To control the climate, oxygen and carbon dioxide are constantly controlled. The data are transmitted to a computer, and the computer controls via specialized software the electronic climate unit and the inflow of nitrogen and fresh air through the air condition (software by Low Oxygen Systems, Berlin-Buch, Germany; hardware by Atlas Copco, Walter Bau and Siemens and Fujitsu-Siemens, all Germany). The nitrogen is produced via a filtration system that eliminates oxygen from pressurized air. Because of the constant air flow, there is a low noise level from the air condition in the room. The level of simulated altitude is usually displaced in the rooms. To keep subjects uninformed about the oxygen level in the room while the air condition was running at all times, this display was covered during the study times and made invisible to the subjects.
Subjects Thirty-two healthy subjects (22 men, 10 women) with a minimum age of 16 years and a minimum body mass index (BMI) of 27 were asked to participate in an 8-week trial with three times 90-min moderate physical exercise. Estimation of sample size was calculated to reach a statistical significance for a change in BMI of one point. Acquisition of subjects was done by local radio announcement and personal contact. Subjects did not receive any kind of reimbursement for participation. All subjects were asked to continue with their normal diet/nutrition which they had before the study.Twenty subjects were finally willing to perform the whole 8-week training period and gave written consent. Subjects were randomly allocated to the hypoxia training and the sham hypoxia training group. Subjects were not told to which group they were allocated. Antropometric data of the subjects in the two groups are shown in Figs. 2 and 3. Both groups were comparable in terms of age and sex.
Fig. 2Mean age (SD), weight, and body mass index (BMI) of the subjects who finished the trial in the hypoxia groupFig. 3Mean Age, weight, and BMI of the subjects who finished the trial in the sham hypoxia groupIn all 20 subjects, spiro-ergometry was performed to determine the max O2 consumption (ml kg−1 min−1) at exercise and the training level for the study set at the heart frequency value at 60% of the max O2 (ml kg−1 min−1). In all subjects, an echocardiography was performed for safety reasons to exclude subjects with heart insufficiency levels NYHA 2–4.
Study parameters Primary study parameter was bodyweight (kg) before and after the trial. Secondary study parameters were: BMI and the laboratory parameters (serum and EDTA blood sample); HbAC1, cholesterol, high- and low-density lipoproteins and triglycerides. Other exercise and blood parameters were not part of this trial but are available on request for reviewers from the laboratory and exercise test data sheets.Blood samples were drawn at the beginning and end of the 8-week trial; bodyweight and height were measured via a regular medical weight scale.
Training period Subjects performed exercise at a heart rate level which corresponded to 60% of the heart rate level at their individual max O2 (ml kg−1 min−1) on three different training (stepper, treadmill, and bicycle ergometer) devices for 90 min on 3 days per week, 8 weeks, either in normobaric hypoxia (15 vol.% O2, equivalent to 2,500-m altitude) or in normobaric normoxia (20 vol.% O2, equivalent to 450-m altitude). For safety reasons, SaO2 was controlled regularly during the training via pulseoximeter (Konica Minolta, Japan) in all subjects, and the subjects were asked for the main symptoms of acute altitude sickness (headache, nausea, and training untypical weakness in the legs). Pulse oximetry was blinded to subjects. Training was under constant control of medical personnel.
Statistical analysis The differences between the two groups were analyzed via Student’s t test, statistical significance determined as p < 0.05 (statistical software used: Excel, Microsoft, Redwood, WA, USA). Data are presented as descriptive data, and values are given as mean values.
Ethics and informed consent The study protocol was approved by the ethical committee of the Paris Lodron University Salzburg. All subjects gave written informed consent to participate in the study. Subjects under 18 were finally excluded; therefore, written consent by the parents was not necessary.
Results
All 20 subjects who were included in the training sessions finished the study without problems. No adverse side effects were reported by the subjects, including no signs of acute mountain sickness in those who exercised in 15.2 vol.% oxygen level.
The average heart rate at 60% O2max for all subjects was 118/min. Subjects kept this heart frequency level stable during exercise (±3/min). This meant that because of the slightly increased heart frequency at rest in hypoxia, the hypoxia group exercised at a lower wattage.
Because of the neutral smell of the air-conditioned air, subjects could not tell if they are in hypoxia or sham hypoxia. More than 50% of the subjects guessed wrongly as to which group they belong.
Weight loss: The hypoxia group lost 1.14 kg in mean; the sham hypoxia group lost no bodyweight at all (p = 0.02, see Figs. 4 and 5). This led to a non-statistical significant tendency in the hypoxia group to reduce the BMI more than the sham hypoxia group (p = 0.33). The number of subjects had been too low to reach statistical significance.
Fig. 4Mean (SD) weight loss after 7 weeks in the hypoxia group vs the sham hypoxia groupFig. 5Weight reduction or increase in the single subjects (matched pairs by age) of the hypoxia vs sham hypoxia group (y-axis = kg, x-axis = number of subject). Weight loss occurred in six subjects of the hypoxia group and in four of the sham hypoxia group, weight increase in two of the hypoxia group and four of the sham hypoxia group, weight stayed unchanged in two subjects of hypoxia (four and eight) and two of the sham hypoxia group (eight and nine)
Lipid levels and HbA1C: None of the differences in lipid levels reached statistical significance. However, the hypoxia group showed little non-statistical significant tendencies to lose more cholesterol (−10.7 mg/ml vs 6.0 mg/ml; p = 0.68), triglycerides, and low-density lipoproteins than the sham hypoxia group, but less high-density lipoproteins (Figs. 6 and 7).
Fig. 6Mean (SD) reduction and increase in triglycerides values of the hypoxia vs sham hypoxia groupFig. 7Mean (SD) reduction in high-density lipoproteins (HDL) in the hypoxia vs sham hypoxia group
The healthy subjects of both groups were not diabetics. They had HbA1C levels on the edge but still in normal values (mean 5.67 and 5.47%), and those levels did not change after the training period of 8 weeks.
Discussion
The here presented study on fat metabolism and weight loss in mild to moderate hypoxia is, to our knowledge, the first one with a sham hypoxia control protocol.
We could show that obese subjects did lose significantly more weight during an 8-week training period with three times per week 90-min low intense exercise when this exercise took place in 15 vol.% O2 hypoxia than in normal air at 450-m altitude (20 vol.% O2). We could also show that there have been non-statistical differences in fat metabolism or at least in serum levels of lipoproteins and triglycerides between the hypoxia training and the sham hypoxia training group.
There are, due to the nature of the study and above that, several limitations, which have to be addressed. The number of subjects is not big enough to show a reduction in BMI, and power analysis shows that we would need about 32 subjects to have a significant BMI reduction. We wanted to get more subjects into the study, but the reduction of subjects to a limited number due to time restrictions and willingness is a common problem in weight loss protocols with obese people. The difference in weight loss in mean with 1.14 kg seems, for 8 weeks of training, not really a lot. However, several subjects did significantly lose more weight in the hypoxia group, and the mean bodyweight loss is not due to natural swings in weight change.
This also happened although the hypoxia group trained at a slightly lower wattage due to a slightly increased heart frequency in hypoxia. We cannot fully exclude that the normoxia group decreased body fat and increased muscle mass due to the slightly higher level of muscle load, but it seems unlikely with this very low intense training.
Because we did not put our subjects on a stringent diet, there might have been the chance that some individuals did eat more than normally and some less. Keeping the subjects on their routine food seemed to us the natural choice of being uninfluenced in the weight change by a special nutritional diet. The other point is that weight loss in hypoxia might be caused by reduced food intake, and that was a part of the study. To let the subjects reduce their food intake, you have to have them on a longer leash. This is especially true if leptin and hypothalamic signals come into play and might let subjects be less hungry, respectively, less hungry for fat. To achieve more accuracy, the only chance would be to exactly control the nutrition of all subjects already before the study and during the training period. To do this, subjects have to be under full supervision all the time. This is an almost impossible achievement in ambulatory care or with independent volunteers.
Our findings are consistent with previous studies which have shown that low intense training in moderate hypoxia leads to weight loss [11–14]. The protocol with a sham hypoxia control group, which did not lose weight with the same amount of exercise, strengthens this hypothesis. A definite reason why this weight loss occurs is still not known. Several investigations could prove that the body reduces fat as a substrate for energy production due to the higher use of oxygen necessary in fat burning [2, 13], but this is still controversially discussed [14]. One of the reasons for a controversial discussion is that so far, nobody did observe subjects on a longer term [14]. Most studies have dealt with exercise in hypoxia for hours or a maximum of a few days, not for 2 months as we did.
In our investigation, the subjects in hypoxia had reduced their triglycerides, their cholesterol, and their low-density lipoproteins and kept their high-density lipoproteins stable, whereas the subjects in normoxia did not do so. However, none of the effects reached statistical significance. Due to the actual literature, this can be explained by several possible causes: Either the fat mobilization is reduced due to hypoxia over time and so the serum levels of lipids are lower at the point of measurement or the increased catecholamine levels during hypoxia lead to reduced lipolysis and a depot of intracellular fat [16, 17].
Most likely, it seems that increased leptin levels lead to a reduced fat intake via food, and that is the reason why triglyceride levels decrease [6, 10]. The answer is still open, and more research is needed to clarify this. Other mediators besides catecholamines, like insulin like growth factor, might influence both fat and carbohydrate metabolism in hypoxia [15].
In conclusion, there is more and more evidence that even mild moderate hypoxia in comfortable simulated altitude, if combined with exercise, leads to weight loss.
Weight loss regimes with exercise and diets or just exercise could be combined with normobaric hypoxia in the future. Future investigations should include whether the stay in moderate hypoxia alone without exercise increases the weight loss and if such a regimen would still be safe.

physical exercise
normobaric hypoxia
weight loss


Engineering eukaryotic protein factories

The biopharmaceuticals market is currently outperforming the pharmaceuticals market and is now valued at US$ 48 billion with an average annual growth of 19%. Behind this success is a 100-fold increase in productivities of eukaryotic expression systems. However, the productivity per cell has remained unchanged for more than 10 years. The engineering of the ER-resident protein folding machinery is discussed together with an overview of signal transduction pathways activated by heterologous protein overexpression to increase cell specific productivities.
Introduction
The demand for therapeutic proteins, i.e. monoclonal antibodies, has been forecast to exceed production capacities (Werner 2004). Moderate increases in product titers and yields are expected to result in significant savings in capital investment and consumer costs (Werner 2004). Over the last decade product titers have increased from ∼20 mg l−1 to 2 g l−1 (Werner 2004; Wurm 2004). These improvements are largely attributable to increases in viable cell densities. However, cell specific productivities remained nearly constant during this time (Wurm 2004).
To improve cell specific productivities the bottleneck for heterologous protein secretion has to be identified and resolved. In expression systems exploiting multiple gene copies or strong promoters the amount of secreted heterologous protein does not increase proportionally with gene copy number (Schröder 2007), messenger ribonucleic acid (mRNA) (Schröder et al. 1999), or even the intracellular amount of the heterologous protein (Schröder and Friedl 1997). In these cells, the protein accumulates in intracellular aggregates (Schröder and Kaufman 2005), associates with the molecular chaperone heavy chain-binding protein (BiP)/glucose-regulated protein of 78 kDa (GRP78)/karyogamy 2 protein (Kar2p) (Schröder and Kaufman 2005), and induces dilation of the endoplasmic reticulum (ER) (Dorner et al. 1989; Gennaro et al. 1991). Therefore, exit of the correctly folded polypeptide chain from the ER is the rate-limiting step for heterologous protein secretion. I will discuss strategies to improve cell specific productivities by engineering protein folding in the ER and signal transduction pathways activated by overexpression of heterologous proteins.
Protein folding in the ER
The ER-resident protein folding machinery has four components: (1) Molecular chaperones assisting other proteins to fold, (2) enzymes such as protein disulfide isomerases (PDIs) and cis-trans peptidyl prolyl isomerases (PPIs), (3) a degradation machinery, and (4) signal transduction pathways that co-ordinate protein folding demand with capacity. At least three hierarchically organized chaperone systems operate in the ER, the heat shock protein of 70 kDa (HSP70) chaperones BiP and luminal HSP seventy 1 protein (Lhs1p)/chaperone in the ER 1 protein/Ssi1p/GRP170/oxygen-regulated protein of 150 kDa (ORP150), the HSP90 chaperone GRP94/adenotin/endoplasmin/tumor rejection antigen glycoprotein of 96 kDa (gp96)/ER protein of 99 kDa/HSP108/Ca2+-binding protein 4/protein kinase of 80 kDa, and the lectin chaperones calnexin/p88, calreticulin/Ca2+-binding protein of 63 kDa (CAB-63)/calregulin/Ca2+-regulated protein of 55 kDa (CRP55)/high affinity Ca2+-binding protein (HACBP), and calmegin/calnexin-t (Fig. 1). BiP works on completely unfolded polypeptide chains and translocates nascent polypeptide chains into the ER, whereas GRP94 and the lectin chaperones work on partially folded substrates.
Fig. 1Hierarchy of chaperone systems in the ER. Abbreviations: Glc = d-glucose, Pi = inorganic phosphate ()
BiP consists of an N-terminal ATPase domain and a C-terminal substrate binding domain. BiP cycles through rounds of adenosine triphosphate (ATP) hydrolysis and adenosine diphosphate (ADP) ATP exchange (Fig. 1), which makes BiP a chaperone foldase (Winter and Jakob 2004). At least six DnaJ or HSP40 co-chaperones stimulate the ATPase activity of BiP and two growth after phage induction E (GrpE) co-chaperones the ADP ATP exchange reaction. In the ADP-bound form BiP has high affinity for unfolded substrates. Substrates bound to BiP are conformationally locked. ADP ATP exchange decreases the affinity of BiP for unfolded substrates. Substrate binding stimulates the ATPase activity of BiP. Thus, protein folding requires ATP (Dorner and Kaufman 1990). BiP and Lhs1p coordinate their activities. Lhs1p is a nucleotide exchange factor for BiP, and BiP stimulates the ATPase activity of Lhs1p (Steel et al. 2004).
Cytosolic HSP90 chaperones are ATP-consuming chaperone foldases (Fig. 1) regulated by several co-chaperones that affect its ATPase activity, load substrates onto HSP90 chaperones, assist in protein folding, and target substrates to degradation pathways (Strudwick and Schröder 2007). GRP94 co-chaperones have not been identified, but other ER luminal chaperones found in complexes with GRP94 may be GRP94 co-chaperones. The KM value of GRP94 for ATP is ∼100 μM and its intrinsic ATPase activity is barely detectable. These data indicate that GRP94 may be a chaperone holdase, a chaperone that binds to unfolded proteins, but does not cycle through ATP hydrolysis cycles. A drop of adenine nucleoside concentrations in stress situations may activate GRP94 (Rosser et al. 2004).
The lectin chaperones share an N-terminal globular oligosaccharide-binding domain and a C-terminal extended hairpin loop, the P domain, which provides the chaperone function (Strudwick and Schröder 2007). Calnexin and calmegin are transmembrane proteins. Calreticulin is a soluble protein. Most proteins entering the ER are glycosylated by addition of the oligosaccharide Glc3Man9GlcNAc2 (Glc = d-glucose, Man = d-mannose, GlcNAc = N-Acetyl-2-d-glucosamine). α-Glucosidases I and II rapidly remove the two terminal d-glucose residues. Calnexin and calreticulin bind to this monoglucosylated oligosaccharide. Removal of the third d-glucose moiety by α-glucosidase II releases the protein from calnexin/calreticulin. Reglucosylation of unfolded proteins by uridine diphosphate (UDP)-glucose:glycoprotein glucosyl transferase (UGGT) triggers repeated interactions with calnexin/calreticulin (Fig. 1). This reglucosylation reaction consumes UDP-glucose. Therefore, the calnexin chaperones are chaperone foldases. The Saccharomyces cerevisiae genome encodes a calnexin homolog, but no homolog for UGGT, suggesting that the lectin chaperones may also function as holdases. Demannosylation of N-linked oligosaccharides trigger export of proteins to the Golgi complex or their targeting for retrotranslocation into the cytosol and proteasomal degradation (Lederkremer and Glickman 2005; Ruddock and Molinari 2006).
An expanding family of PDIs catalyzes the formation and isomerization of disulfide bonds. Disulfide bond formation requires the regeneration of oxidized PDI catalyzed by the flavin adenine dinucleotide (FAD)-dependent oxidases ER oxidation 1 protein (Ero1p)/ERO1-Lα, ERO1-Lβ and essential for respiration and viability 2 protein (Erv2p) (Fig. 2). The final electron acceptor is molecular oxygen. Oxidative protein folding accounts for ∼25% of all reactive oxygen species (ROS) formed in a cell (Tu and Weissman 2004) and is increased by ER stress (Haynes et al. 2004). Disulfide bond isomerization is independent of the Ero1p oxidases. Reduced glutathione provides reducing equivalents to remove incorrectly formed disulfide bonds. PDI has chaperone holdase activity that is independent of its protein disulfide isomerase activity (Wilkinson and Gilbert 2004) and redox-regulated foldase activity (Tsai et al. 2001).
Fig. 2Disulfide bond formation and isomerization reactions catalyzed by PDI
Slowly folding proteins or folded incompetent proteins have to be removed from chaperone cycles to prevent them from poisoning the protein folding machinery. This is achieved by targeting these proteins for retrotranslocation into the cytosol and degradation by the proteasome in a process called ER associated protein degradation (ERAD) (Yoshida 2007) (Fig. 3). Proteins destined for ERAD are recognized by BiP, PDI, and lectin sensors such as ER-degradation enhancing α-mannosidase-like proteins 1–3 (EDEM1–3)/homologous to mannosidase I 1 protein (Htm1p)/mannosidase-like 1 protein (Mnl1p) and osteosarcoma-9 (OS-9)/yeast OS-9 homolog protein (Yos9p) (Gauss et al. 2006a). The lectin sensor EDEM extracts unfolded proteins from the calnexin cycle. ERAD substrates are then retrotranslocated into the cytosol via the secretory 61 (SEC61) channel or a channel formed by derlin proteins, ubiquitinated by action of ubiquitin-conjugating enzymes (E2 enzymes) and ubiquitin ligases (E3 enzymes), deglycosylated, and degraded by the proteasome. Redundancy exists in the ubiquitination machinery involved in ERAD. In yeast and mammalian cells at least three distinct E2·E3 complexes are present in the ER membrane. The substrate specificities of these complexes, as well as their substrate selection rules are poorly understood. In yeast, the E3 ligase Hrd1p co-operates with Hrd3p and Yos9p to select its substrates (Gauss et al. 2006b). Another selection criterion is the time an unfolded protein spends in foldase cycles. In the calnexin cycle, demannosylation of N-linked oligosaccharides generates glycostructures recognized by lectins such as EDEM1–3. Association of cytosolic HSP70s with the nucleotide exchange factor B cell leukemia/lymphoma 2 (Bcl-2) associated athanogene protein 1 (BAG-1) (Alberti et al. 2003) and of cytosolic HSP90s with the ubiquitin ligase carboxyl terminus of heat shock cognate protein of 70 kDa (HSC70) interacting protein (CHIP) (Höhfeld et al. 2001) targets proteins for proteasomal degradation. Association of the DnaJ co-chaperone 58 kDa inhibitor of double-stranded RNA activated protein kinase (PKR, p58IPK) with HSP70 chaperones at the translocation pore targets proteins, whose translocation into the ER is stalled, for proteasomal degradation (Oyadomari et al. 2006). If similar targeting mechanisms for proteins folding slowly in the BiP foldase cycle exist, is currently unknown.
Fig. 3ER associated protein degradation. The exact nature of the retrotranslocation channel is unknown. The list of E2 and E3 enzymes catalyzing ubiquitination of ERAD substrates is not exhaustive. Abbreviations: NEF = HSP70 co-chaperone, Ub = ubiquitin
Engineering of chaperone machineries
Engineering of chaperone holdases should be straightforward. Holdases hold onto an unfolded protein and act in an equimolar ratio to the unfolded substrate, but are unable to support folding reactions. Holdases may provide a buffer capacity to prevent aggregation of unfolded proteins. Co-overexpression of chaperone foldases may be beneficial to overexpression of holdases (Smith et al. 2004; Zhang et al. 2006b). Overexpression data for calnexin/calreticulin and PDI are most easily explained by assuming that this buffer capacity is increased. Calnexin or calreticulin overexpression generally improved heterologous protein secretion (Chung et al. 2004; Conesa et al. 2002; Kato et al. 2005). Overexpression of one cycle constituent should not increase cycle capacity, if this constituent is not limiting, and if constituent levels are not co-regulated. The hypothesis that calnexin or calreticulin are limiting for the calnexin cycle seems unlikely, because of the high abundance of these proteins. Alternatively, overexpression of calnexin or calreticulin may inactivate lectins such as EDEM (Molinari et al. 2003; Oda et al. 2003), which target unfolded proteins for ERAD. Further, elevated calnexin or calreticulin levels not supported by concomitant increases in activities of other calnexin cycle components, may convert these surplus amounts of calnexin and calnexin into holdases or inactive chaperones.
PDI overexpression increased secretion of some, but not all, heterologous proteins (Butz et al. 2003; Damasceno et al. 2006; Inan et al. 2006; Kato et al. 2005; Mohan et al. 2007; Schröder 2007; Zhang et al. 2006b). PDI overexpression provides increased holdase activity in the ER. For example, overexpression of catalytically inactive PDI improved heterologous protein secretion (Hayano et al. 1995). PDI overexpression also improved secretion of heterologous proteins containing no disulfide bonds (Powers and Robinson 2007; Smith et al. 2004; Vad et al. 2005). Substrate selectivity of PDI may explain why PDI overexpression failed to increase heterologous protein secretion in some studies (Butz et al. 2003; Damasceno et al. 2006; Mohan et al. 2007; Schröder 2007). If this is the case, co-expression of an alternative holdase, i.e. GRP94, may yield more consistent results for different heterologous proteins. Alternatively, increased rates of disulfide bond formation or isomerization in PDI overexpressing cells may explain increased heterologous protein secretion. As an enzyme, PDI should function at substochiometric ratios to its substrates. In disulfide bond formation, regeneration of oxidized PDI may become rate-limiting in PDI overexpressing cells. PDI is an abundant ER protein, making it unlikely that its overexpression can significantly augment its catalytic activities.
Engineering of foldases seems more difficult, because of the complex regulation of these chaperones (see above). BiP overexpression increased heterologous protein secretion for about half of the heterologous proteins studied (Chung et al. 2004; Damasceno et al. 2006; Dorner et al. 1992; Hsu and Betenbaugh 1997; Kim et al. 2003; Schröder 2007; Smith and Robinson 2002; Zhang et al. 2006b), but can also have negative effects (Dorner et al. 1992; Schröder 2007). In cells overexpressing BiP one of its co-chaperones, Lhs1p, or ER luminal ATP may become limiting, converting BiP into a holdase or inactive chaperone. This may target proteins towards ERAD, stall translocation, convert Lhs1p into a holdase or inhibit Lhs1p. Increased BiP activity may stall the GRP94 and/or calnexin–calreticulin chaperone machineries, because of the hierarchy of ER luminal chaperone systems. This again may target heterologous proteins for ERAD. BiP overexpression attenuates the unfolded protein response (UPR, see below). Thus, BiP levels in WT and BiP overexpressing cells expressing a heterologous protein may not be dramatically different. Further, BiP overexpressing cells may experience an imbalance in their chaperone machineries, because the UPR co-ordinates expression of several chaperones. Moreover, if overexpression of BiP shifts the folding bottleneck to the GRP94 and calnexin chaperone systems, upregulation of these chaperone systems by the UPR may be blunted, because of suppression of UPR activation by elevated BiP levels.
Unfolded protein response (UPR)
ER stress is the perturbation of the balance between the folding capacity and the folding demand imposed on the ER. To restore ER homeostasis, the UPR activates expression of chaperone, PDI, PPI, and ERAD genes, attenuates transcription of genes encoding secretory proteins and general translation, induces phospholipid synthesis, and induces an inflammatory response. Apoptosis is induced if these actions fail to restore ER homeostasis or in response to prolonged ER stress (Schröder and Kaufman 2005).
The ER membrane of higher eukaryotes harbors at least three classes of transmembrane proteins activated by ER stress, basic leucine zipper (bZIP) transcription factors synthesized as type II transmembrane proteins, of which the best characterized are activating transcription factor 6α (ATF6α) and ATF6β/cyclic adenosine monophosphate response element binding protein (CREB)-related protein (CREB-RP)/G13, the protein kinase eukaryotic translation initiation factor 2α (eIF2α) kinase 3 (EIF2AK3)/pancreatic eIF2α kinase (PEK)/PKR-like ER kinase (PERK), and the protein kinase-endoribonucleases ER to nucleus signaling 1α (ERN1α)/inositol-requiring 1α (IRE1α) and IRE1β (Fig. 4). Several ATF6 paralogs, CREB3, CREB4, CREB-H, box B binding factor 2 (BBF2), and old astrocyte specifically induced substance (OASIS) have recently been described. In unstressed cells, these stress sensors are bound to BiP and kept in an inactive state. Upon ER stress, BiP is released coinciding with their activation. Whether BiP release is triggered by sequestration by unfolded proteins, conformational changes in the ER luminal domains, or reactivation of the BiP ATPase cycle is currently being debated (Kimata et al. 2004; Shen et al. 2005). ER stress also alters interactions of pro- and antiapoptotic Bcl-2 proteins at the ER membrane. These proteins regulate ER luminal Ca2+ homeostasis, ER morphology, ER stress signaling, and apoptosis induced in response to ER stress (Hetz 2007).
Fig. 4Signal transduction pathway in the UPR. In the human UPR caspase 4 substitutes for caspase 12. In yeast and filamentous fungi, the IRE1-XBP-1 (Hac1p/HACA) pathway is the only known UPR signal transduction pathway. [Reprinted in modified form with permission from Bentham Science Publishers from Schröder M, Kaufman, RJ (2006) Divergent roles of IRE1α and PERK in the unfolded protein response. Curr Mol Med 6:5–36]
BiP release from ATF6 induces translocation of ATF6 to the Golgi complex where its bZIP transcription factor domain is proteolytically released from the Golgi membrane by site 1 and 2 proteases (S1P and S2P) (Fig. 4). This cytosolic fragment translocates to the nucleus and induces expression of chaperone genes (Strudwick and Schröder 2007). The functions of the ATF6 paralogs in the ER stress response is less well understood. OASIS contributes to activation of the BiP gene (Kondo et al. 2005), and ATF6·CREB-H heterodimers activate inflammatory genes (Zhang et al. 2006a).
BiP release from PERK triggers oligomerization and activation of the protein kinase domain of PERK. PERK phosphorylates the bZIP transcription factor nuclear factor erythroid 2 (NF-E2) related factor 2 (NRF2) and eIF2α (Strudwick and Schröder 2007) (Fig. 4). NRF2 phosphorylation induces its translocation to the nucleus, where a NRF2·ATF4 heterodimer activates transcription of antioxidant response genes (Harding et al. 2003) to counteract ROS formation by repeated folding attempts of nascent polypeptide chains in ER stressed cells. eIF2α phosphorylation attenuates general translation and decreases the influx of nascent unfolded polypeptide chains into the ER. It also clears short-lived proteins from the cell, i.e. D-type cyclins, resulting in cell cycle arrest in G1 phase, and of inhibitors of nuclear factor κB (NF-κB) (IκB), activating the pro-inflammatory transcription factor NF-κB. eIF2α phosphorylation also induces preferential translation of capped mRNAs containing several short upstream open reading frames (uORFs) and cap-independent translation of mRNA via internal ribosomal entry sites (IRES). Currently, the only known mRNA whose translation is stimulated by ER stress is the mRNA encoding the bZIP transcription factor ATF4. ATF4 induces expression of CCAAT enhancer binding protein (C/EBP) homologous protein (CHOP)/CHOP-10/growth arrest and DNA damage-inducible gene 153 (GADD153). CHOP represses transcription of anti-apoptotic BCL-2 (McCullough et al. 2001) and induces expression of pro-apoptotic tribbles-related protein 3 (TRB3) (Ohoka et al. 2005). Translational attenuation by PERK is transient and countered by several eIF2α phosphatases. The regulatory subunit of protein phosphatase 1 (PP1), GADD34, is induced late in ER stress by ATF4, and targets PP1 to eIF2α.
Upon BiP release, IRE1 autophosphorylates in trans and activates its endoribonuclease domain (Strudwick and Schröder 2007). Activated IRE1 cleaves exon-intron junctions in the mRNAs encoding the bZIP transcription factors homologous to ATF/CREB1 protein (Hac1p) in yeast, HACA in filamentous fungi, and X box-binding protein 1 (XBP-1)/hepatocarcinogenesis-related transcription factor (HTF)/tax-responsive element binding protein 5 (TREB5) in metazoans (Fig. 4). In yeast, the HAC1 exons are ligated by transfer RNA ligase. Both transcription factors activate expression of genes encoding ER-resident molecular chaperones, protein foldases, and genes encoding proteins involved in ERAD. Hac1p and XBP-1 also stimulate phospholipid biosynthesis. Spliced Hac1p represses genes under control of the transcriptional regulator unscheduled meiosis 6 protein (Ume6p), including the early meiotic genes and a large array of genes involved in carbon and nitrogen metabolism. This function of spliced Hac1p may explain the slow growth phenotype of yeast cells overexpressing Ire1p or spliced Hac1p (Mori et al. 2000). Attenuation of metabolism by Hac1p in yeast may substitute for attenuation of general translation by PERK. XBP-1 activation is delayed compared to ATF6 and PERK activation (Yoshida et al. 2003), allowing for an early folding only phase in the UPR followed by a folding and degradation phase. In mammals, phosphorylated IRE1 sequesters tumor necrosis factor receptor associated factor 2 (TRAF2) from pro-caspase 12, resulting in clustering and activation of this caspase and initiation of apoptosis (Strudwick and Schröder 2007) (Fig. 4). TRAF2 recruits apoptosis signal-regulating kinase 1 (ASK1) and IκB kinase to IRE1. ASK1 activates the mitogen-activated protein (MAP) kinases p38 and ju-nana (jun) N-terminal kinases (JNKs). p38 and JNKs phosphorylate and potentiate pro-apoptotic transcription factors, i.e. CHOP and c-Jun, and phosphorylate and inhibit anti-apoptotic Bcl-2 and Bcl-xL to induce apoptosis (Szegezdi et al. 2006). Inhibition of JNK phosphatases by ROS formed in oxidative protein folding also contributes to JNK activation (Kamata et al. 2005). Association of the pro-apoptotic Bcl-2 family proteins Bax and Bak with the cytosolic portion of IRE1 is required for efferent IRE1 signaling, i.e. XBP-1 mRNA splicing and JNK1 activation (Hetz et al. 2006).
Engineering of the UPR
Engineering of the UPR holds promise to increase concentrations of several chaperones in a functionally meaningful ratio, thus preventing inadvertent conversion of chaperone foldases into holdases, or even inactivating chaperones because of lack of concomitant increases in co-chaperones and co-factors. The UPR is as much a survival response as it is a response to boost protein secretion. Clearly, some aspects of the physiological UPR are not desirable in an expression system, for example inhibition of general translation by PERK, stimulation of ERAD and apoptosis. Overexpression of spliced Hac1p or its filamentous fungi homolog HACA increased heterologous protein secretion in Saccharomyces cerevisiae and Aspergillus niger (Valkonen et al. 2003a, b). In Chinese hamster ovary (CHO)-K1 cells XBP-1 overexpression improved secretion of several heterologous proteins (Ku et al. 2007; Tigges and Fussenegger 2006). In yeast and filamentous fungi, secretion of several heterologous proteins was not improved or even decreased by overexpression of Hac1p (Gasser et al. 2006; Rakestraw and Wittrup 2005; Valkonen et al. 2003a). One drawback of some of these studies is that wild-type (WT) cells with a functional UPR are compared to WT cells overexpressing spliced Hac1p, which may explain why in some cases only marginal differences between the two cell types have been observed. The more consistent effect of spliced XBP-1 overexpression on protein secretion (Ku et al. 2007; Tigges and Fussenegger 2006) may be caused by a preferential activation of UPR signaling pathways that stimulate cellular functions beneficial for heterologous protein secretion, i.e. increased chaperone gene expression. This increased ER-resident chaperone machinery attenuates all UPR signaling, and most importantly, those UPR signaling pathways likely to decrease protein production, for example general translation inhibition by PERK, stimulation of ERAD and of apoptosis (Özcan et al. 2004).
Conclusions
Engineering of chaperone systems by overexpressing a single component of the ER-resident protein folding machinery has overall yielded mixed results. Our basic understanding of protein folding in the ER is still incomplete. Addressing these open questions should underpin engineering approaches to improve the performance of chaperone systems. A more detailed understanding of chaperone function and regulation should inform future work to improve chaperone systems. Co-expression of different holdases or targeting of heterologous or cytosolic holdases to the ER may yield more consistent improvements for different heterologous proteins. The function of overexpressed chaperones may not be the same as at their normal physiological concentrations, because of the lack of a corresponding increase in co-chaperones and co-factors. To improve foldase function, concomitant elevation of BiP, its co-chaperones, and ATP levels should be attempted.
Engineering of the UPR suggests that mimicking an UPR by expression of its activated signaling molecules does not consistently improve productivities. Dissection of UPR signaling activities may be necessary to improve heterologous protein secretion. To place engineering of the UPR on an informed basis, we need to understand the UPR in more detail. It is still not clear what the most upstream events in activation of the UPR are, how UPR signaling integrates into cellular signaling, and how the UPR decides between a prosurvival and an apoptotic response to ER stress. Cell engineering also needs to address the potential problem that increased oxidative protein folding may be inherently toxic to cells, because of elevated cellular ROS levels.

endoplasmic reticulum associated protein degradation


Minimising treatment-associated risks in systemic cancer therapy

Aim of the review To review the consequences of drug-related problems (DRP) in systemic cancer therapy and identify specific contributions of the pharmacist to minimise treatment-associated risks. Method Searches in PubMed, Embase and the Cochrane Library were conducted. Bibliographies of retrieved articles were examined for additional references. Only papers in English between 1980 and 2007 were included. Results In systemic cancer therapy there is an enormous potential for DRP due to the high toxicity and the complexity of most therapeutic regimens. The most frequently reported DRP can be classified into adverse effects, drug–drug interactions, medication errors, and non-adherence. Pharmacists have enhanced efforts to assure quality and safety in systemic cancer therapy together with other health care providers. In consequence, oncology pharmacy has evolved as a novel specialist discipline. The endeavour to merge and co-ordinate individual activities and services of the pharmacist has led to pharmaceutical care concepts which aim at offering novel solutions to the various DRP. Conclusion Pharmaceutical care for cancer patients should be developed within research projects and integrated into disease management programs in order to ensure broad implementation.
Impact of findings on practice
There is an enormous potential for drug-related problems in cancer therapy
The most frequently reported drug-related problems in systemic cancer therapy are adverse effects, drug–drug interactions, medication errors, and non-adherence
Pharmacists can contribute substantially to risk minimisation in systemic cancer therapy by adding specific drug-related knowledge to the treatment team and offering patient-related services
Pharmaceutical care for cancer patients offers novel solutions to the various drug-related problems and should be further developed in research projects
Introduction
In systemic cancer therapy, drug regimens are administered following established protocols which have been carefully evaluated in clinical trials. The administration of supportive medication is not as standardised as with antineoplastic therapy. Major components of the supportive therapy are selected by the general practitioners or the patients themselves rather than by the oncologist. Furthermore, patients tend to see more than one physician involved in the cancer care process as well as alternative practitioners. Patients are also exposed to a tremendous variety of drugs which are available to the customer without prescription (over-the-counter, OTC).
The more complex drug therapy is, the higher the risk of experiencing drug-related problems (DRP) such as adverse effects, interactions, medication errors, and non-adherence. Drug-related problems in cancer chemotherapy can have severe consequences originating from the high toxicity and narrow therapeutic range of anticancer drugs. The tragic case of Betsy Lehman who died from an overdose of cyclophosphamide at the Dana Faber Cancer Center in Boston demonstrated this risk painfully [1]. It must be the goal of all health care providers to minimise treatment-associated risks as much as possible.
Over the last few decades the pharmacy profession has experienced a change from traditional drug-oriented toward patient-oriented services. In oncology, pharmacists have established central services for compounding cytotoxic drugs and started to offer industry-independent drug information for physicians and patients as well as therapeutic drug monitoring for critical dose drugs, e.g. aminoglycosides in neutropenic patients. As the specialist knowledge of pharmacists in this field has increased continuously “oncology pharmacy” has evolved into a new pharmaceutical discipline with its own curriculum. In 1995 the International Society for Oncology Pharmacy Practitioners (ISOPP) was founded. The aim of the society is “to determine the optimal medical treatment for cancer patients, thereby improving their quality of life”. Currently, the concept of pharmaceutical care is being adapted to the needs of the cancer patient as a further step to optimising individual drug therapy [2].
The intention of this review is to summarise possible solutions for DRP in oncology focussing on the specific tasks of the pharmacist.
Aim of the review
It was the aim of this article to review and analyse the most frequent DRP and their consequences in systemic cancer therapy. Moreover, specific contributions of the pharmacist to minimise treatment-associated risks should be identified.
Method
Searches in PubMed, Embase and the Cochrane Library were conducted. Database terms included DRP, adverse effects, drug–drug interactions, medication errors, adherence, compliance, pharmaceutical care in connection with oncology or cancer. Bibliographies of retrieved articles were examined for additional references. Only papers in English between 1980 and 2007 were included.
Results
Various DRP can occur in the treatment path of systemic cancer therapy (Table 1). In a recently published study conducted on an oncology ward of a Swedish hospital 114 DRP in 58 patients were identified indicating their high incidence in cancer patients [3]. In this section, major problems related to adverse effects, drug–drug interactions, medication errors, and non-adherence are reviewed separately.
Table 1Drug-related problems can originate from several steps of the treatment path
Adverse effects
According to the World Health Organization (WHO) an adverse drug reaction (ADR) is defined as a response to a drug which is noxious and unintended, and which occurs at doses normally used or tested in man for prophylaxis, diagnosis or therapy of disease. In cancer chemotherapy such ADRs are strongly connected to the treatment itself. Because of the fact that most cytotoxic agents cannot distinguish between normal and neoplastic cells, most ADRs seem to be unavoidable. They are often accepted not only by patients but also by health care providers.
In order to illustrate the most common ADRs in oncology patients in a cancer centre in Australia were observed and the incidence as well as the predictability, preventability, and severity of the occurred ADRs were assessed. Among the ten most common ADRs constipation ranked first but was connected directly to the use of opioids rather than to cytotoxic chemotherapy. Nausea and vomiting, fatigue, alopecia, drowsiness and myelosuppression ranked second to sixth. Of these ADRs 88% were predictable and about 50% even probably preventable, because of inadequate use of preventative measures [4].
As well as the incidence of ADRs in modern chemotherapy the patients’ perceptions of the impact of these ADRs on well being and quality of life are increasingly taken into account. Patient perceptions have changed markedly over the last two decades. Whereas in 1983 physical symptoms such as nausea, vomiting and hair loss were most troubling from the patients’ point of view, in 2002 psychosocial complaints ranked among the top ten symptoms, with the complaint “affects my family or partner” rated as the most severe ADR. Alopecia and fatigue ranked second and third place [5, 6].
As a consequence, the management of ADRs has become a focus of clinical research and new drugs such as aprepitant, a neurokinin-1-receptor antagonist for the prevention of nausea and vomiting, were developed [7]. Despite these new therapeutic options the minimisation of toxic effects of chemotherapy still seems to be a challenging task.
The first and most important step to a better management of ADRs is the formulation and implementation of evidence-based clinical practice guidelines for the different symptoms in a multidisciplinary approach. Several studies have shown a positive effect of guidelines for antiemetic prophylaxis and therapy on both clinical and economic outcomes [8–11]. The most important influencing factors seem to be the appropriate use of 5-HT3 receptor antagonists and the application of oral drug formulations. Nevertheless, altering the usage patterns of physicians to comply with evidence-based guidelines seems to be difficult. Methods such as the use of computerised decision support systems and educational outreach mechanisms appear to be most effective particularly when used in combination [12]. The pharmacist can support and promote the adherence to guidelines [13].
A second step to a better management of ADRs is the implementation of standardised chemotherapy order forms that also contain supportive care medication. By using standardised order forms a more appropriate prescribing of antiemetics based on the level of emetogenicity of administered chemotherapy and thus a reduction in drug expenditure can be achieved [14]. Such order forms should be elaborated following a multidisciplinary approach including physicians, pharmacists and nurses.
Drug–drug interactions
The large number of prescribed drugs administered to cancer patients leads to a high potential for drug–drug interactions. As indicated previously many cancer patients also use over-the-counter medication as well as alternative and complementary treatment options. Frequently, cancer patients suffer from concomitant chronic diseases that require the intake of other drugs, further increasing the risk of interactions. This problem is often underestimated in oncology as highlighted in several review articles [15–18]. Moreover, drug–food, drug–disease and drug–diagnostics interactions have to be considered that are, however, beyond the scope of this article.
Drug–drug interactions must be avoided as they can lead to overdosing or underdosing of anticancer drugs with the consequence of toxicity or a loss of effectiveness, respectively. However, not all published interactions are also clinically relevant. A rational and safe drug therapy includes a check for potential drug–drug interactions based on the individual medication and a decision whether and how identified drug–drug interactions have to be considered or not.
Knowledge of the mechanisms of interactions is crucial to assess the clinical relevance of an interaction. Drug–drug interactions are usually classified as pharmaceutical, pharmacokinetic or pharmacodynamic interactions (Table 2). The risk of interactions can differ substantially between individual drugs, even within the same class of drugs. This can be considered when selecting a particular drug for an individual patient.
Table 2Classification of drug–drug interactionsPharmaceutical interactions Mostly physical or chemical incompatibilities, e.g. chemical reactions or precipitations due to drug admixtures.Pharmacokinetic interactions Occur at the level of drug absorption, distribution, excretion, and metabolism. Frequently, the cytochrome P450 metabolising enzyme system and drug transporters such as P-glycoprotein are involved in such interactions.Pharmacodynamic interactions Directly related to the desired or undesired drug effects, e.g. the antitumoral effect or drug-associated toxicity.
For some drugs with a high potential for interactions, therapeutic drug monitoring can be useful to detect and control interactions. The advantage of this approach is that the dosage of the drug of risk can be adapted to the measured individual plasma concentrations. Thus the treatment of both interacting drugs may be continued. Examples are anticonvulsant, antidepressant and antifungal drugs which are often administered to cancer patients as supportive medication [19].
In general, it is almost impossible for the physician to keep in mind all of the interaction mechanisms and potential consequences for the individual patient. Regular interaction checks by the pharmacist may solve this problem as he is the only health care professional who may have an overview of the drugs prescribed by various physicians and the medication taken by the patient on his own initiative. The avoidance of drug–drug interactions should be regarded as a multidisciplinary task [20].
The basis for each interaction check is to take the medication history of each patient and to update it regularly. Nowadays software tools are available facilitating rapid interaction checks and providing information on the mechanism and clinical relevance of an interaction. Each hospital and community pharmacy should have access to at least one of these tools.
Medication errors
In addition to the DRP described above, there are numerous risks for the occurrence of medication errors along the therapeutic path in oncology. A medication error is defined as any preventable event that may cause or lead to inappropriate medication use or patient harm, while the medication is in the control of the health care professional, patient, or consumer [21]. In 2000 the American Institute of Medicine published their report “To err is human: Building a Safer Health System” which analysed flaws in the health system and offered suggestions for its improvement [22]. Based on the results of a study conducted in Utah and Colorado in 1992 an incidence of about 3% adverse events in hospitalised patients was found, of which the majority of the non-operative events were adverse drug events (ADE) [23]. The authors concluded that iatrogenic injury continues to be a significant public health problem. Due to the narrow therapeutic range of many anticancer drugs, the impact of an ADE is more serious (and at worst lethal) compared to other systemically administered drugs. Antineoplastic agents were found to be the second most common group of drugs which caused lethal medication errors [24]. As well as quality and safety issues, the reduction of medication errors has a financial effect.
The first source of medication errors is the prescription and ordering process: the wrong protocol may be chosen, the cumulative dose may be mixed up with the single dose, the route of administration may not be clearly indicated or misinterpretation of the physicians’ handwriting may lead to errors. A study carried out in a cytotoxic preparation unit in a French hospital revealed in only a 6 month period more than 300 medication errors out of 1,262 prescriptions for 285 patients. Most errors (>70%) were found to be simple physicochemical incompatibilities, e.g. incompatibility of the drug with the used matrix. However, also over- and underdosage as well as wrong medications were identified [25]. The authors conclude that most of these errors could possibly be avoided by a computerised prescription network. Computer-assisted solutions enable physicians to order the medication electronically which is nowadays one way to reduce the incidence of those errors. Mekhjian et al. evaluated the benefits of physician order entry systems and found that transcription errors could be reduced and speed in the ordering process could be improved. Even the duration of the stay in hospital could be reduced by the system [26]. Particularly in oncology, the computerised physician order entry (CPOE) has obvious advantages. A recent study has shown that CPOE in electronic medical records improves completeness of the medical record compared to paper charts independent of regimen complexity [27]. Prepared protocols facilitate correct ordering and double checking by the programme itself and by the pharmacist responsible. In addition, supportive medication, such as hydration or the antiemetic or antiallergic medication can be automatically proposed depending on the selected regimen. Moreover, the risk of misinterpretation of handwriting is eliminated. Recently, a CPOE system with integrated clinical decision support has been implemented in Ontario (Canada). This system, for example, alerts the clinician when maximum cumulative doses are reached, calculates body surface area, and estimates the creatinine clearance of the patient [28]. With all the advantages of these systems it has to be taken into account that new risks may arise from those new processes such as information errors due to the lack of clearness on the monitor [29].
The next step after ordering, the compounding of cytotoxic drugs also holds various risks, especially when performed on the ward. The implementation of central cytotoxic preparation units in pharmacy departments in the late 1980s has been one of the first measures to standardise the process in order to increase safety. Nevertheless, of 30,819 preparations surveyed in a study on incidence and risk factors of preparation errors 140 were found to be defective (0.45%) [30]. Less than half of those cases were classified as major errors including wrong dosage, wrong labelling or the use of incompatible diluents. Although the incidence is fairly low it must still be the aim to reduce this further by analysing the risk factors such as workload. Centralisation itself offers many more options for safety improvement. Standardised pharmaceutical validation leads to an increased interception of medication errors in antineoplastic treatment. This can be explained by the improved knowledge of the pharmacists involved in the validation process. In consequence of a standardised pharmaceutical validation process in the hematology–oncology department of a 550-bed university hospital the number of detected medication errors that did not reach the patients could be increased by 41% (from 14.08 medication errors/1,000 patient days to 19.83 medication errors/1,000 patient days) in the second year after the introduction of pharmacists in the multidisciplinary oncology team [31]. Using their expertise, pharmacists can facilitate protocol development and adherence, dose verifications and education of other health care practitioners [32].
Finally, storage and administration errors of cytotoxic drugs form a group of major medication errors with serious consequences for the patient. Therefore, it is mandatory that the assigned staff are well trained in all aspects of chemotherapy storage and administration. For many drugs, special storage conditions, e.g. refrigeration or protection from light, have to be considered. Detailed knowledge of the administered drugs is also very important in order to educate the patient and to react appropriately when incidents such as extravasations occur. In case of extravasation the necessary measures have to be initiated. Different drugs require different measures which the oncologically trained staff needs to know. An extravasation kit including essential items for first aid must be made available on site [33]. Incorrect or delayed treatment can cause serious damage to the patient, resulting in even the loss of a limb.
Various surveys have been undertaken in order to find solutions for the prevention of medication errors. The non-punitive reporting of errors is a necessary requirement to be able to detect, analyse and consequently minimise the incidence and severity of medication errors in oncology. Safety measures with error reporting and analysis have been developed and implemented in order to improve the entire system [34]. The most effective improvements of treatment performance have been achieved by multidisciplinary system approaches integrating physicians, pharmacists and nurses [2, 32, 34–36].
Non-adherence
Adherence (or compliance) is generally defined as the extent to which a person’s behaviour, taking medication, following a diet, and/or executing lifestyle changes, corresponds with agreed recommendations from a health care provider [37]. Non-adherence strongly compromises the success of a patient’s therapy, results in additional, potentially unnecessary, diagnostic and therapeutic procedures and thus generates further costs and possibly health problems as a consequence of the treatments themselves. It is important to note that the traditional concept that patients are solely responsible for taking their medication (and therefore non-adherence is a patient-driven problem) is misleading. The WHO understands adherence as a phenomenon consisting of five dimensions (Table 3). The magnitude of this problem is further emphasised by the fact that adherence rates for many long-term drug therapies range from only 40 to 78% [38, 39].
Table 3The five dimensions of adherence and examples of associated factors (adapted from [34]) Social/economic factorsHealth care team/system-related factorsCondition-related factorsTherapy-related factorsPatient-related factorsEconomic statusPatient-provider relationshipSeverity of symptomsComplexity of regimenAnxiety about side effectsCultural beliefsEducation of providersLevel of disabilityTreatment durationPatients’ motivationIlliteracyCapacity of systemRate of progressionChanges in treatmentPatients’ expectationsAgeDuration of consultationsCo-morbiditiesSide effectsForgetfulnessDistance from treatment centerMedication distribution systemAvailability of effective treatmentsPrevious treatment failuresPatients’ knowledge about illness
It is a difficult task to assess patient adherence reliably. Patient self-reports, rates of prescription refills, patient diaries or pill counts as the sole basis for the measurement of adherence have been shown to be inadequate [38–40]. It has been demonstrated that these methods overestimate the degree to which patients adhere to their tamoxifen regimen [40]. The use of microelectronic adherence monitoring (Medication Event Monitoring System, MEMSTM, Fig. 1) provides a valuable estimate of the timing of events and insights into patients’ behaviour in taking medication. However, to prove actual intake of the medication, additional plasma concentration monitoring would be necessary. To obtain an optimal measurement of patient adherence, a combination of several methods is suggested [38].
Fig. 1Medication event monitoring system, MEMSTM: The pill bottle cap contains a micro-electronic circuit that registers when the bottle is opened. This data may then be transferred to a computer via a reading device
Studies conducted in various disease areas have shown that a wide variety of methods to improve adherence is available. A recently published review classifies these methods into four general categories: patient education, improved dosage, increased hours when clinics are open, and improved communication between health care providers and patients [38]. All four categories require a collaborative approach including all members of the multidisciplinary health care team. Due to their position in the chain of health care providers, this requires the contribution of pharmacists, not only in the hospital setting but also in community pharmacies [41]. It has been shown that no single intervention strategy appears consistently more effective than another. Successful methods are complex and comprehensive as well as labour-intensive [38, 42]. The Cochrane Review on Interventions to Enhance Medication Adherence concludes that there is no evidence that low adherence can be “cured” and hence efforts to improve adherence must be maintained for as long as the treatment is needed [43].
Cancer patients seem to benefit especially from interventions towards an optimised adherence, resulting in improved outcomes [42]. Research investigating adherence in oncology settings has been mostly focussed on palliative care and supportive medication because chemotherapy has mainly been administered intravenously in hospitals or clinics. However, with the increasing availability and importance of oral anticancer drugs such as capecitabine, etoposide, vinorelbine, erlotinib and sorafenib, patients increasingly take over the responsibility for the correct administration of their prescribed therapy. Although cancer patients might be more adherent than other patient groups due to a high level of motivation, the slightest non-adherence can endanger the therapeutic goals [39].
Only few studies have been published investigating the level of adherence of cancer patients. In a cohort of outpatients receiving chemotherapy for haematologic malignancies adherence to the allopurinol and prednisolone prescription was measured based on plasma concentrations. It was shown that control patients without intervention were adherent only 17% of the time with allopurinol and 27% of the time with prednisone. Three specially developed intervention packages including education and home visits resulted in increased adherence rates of 44–48% and 33–38% of the time for allopurinol and prednisone, respectively [44]. Another study assessed adherence in patients with breast cancer receiving oral cyclophosphamide and found that 43% of the patient population met criteria for non-adherence according to both behavioural and dosage definitions [45]. In a recently published study in a cohort of 2816 women adherence to tamoxifen treatment was investigated using prescription refill data: 22.1% of the patients exhibited a discontinuation of treatment (non-persistence) within 1 year [46]. An open controlled trial is currently undertaken at the University of Bonn investigating the impact of intensified pharmaceutical care provision on the adherence of patients receiving oral capecitabine using the MEMS™-technology. Pharmacists’ interventions include the extensive provision of drug information to the patients and health care professionals, routine checks for drug interactions, preparation of written drug intake plans for patients, regular pharmacist consultations and structured documentation of the pharmaceutical care process [47].
Discussion
The recognition of the described risks of the individual patient associated with complex drug therapies has led to the development of a conceptual framework for an advanced pharmacy practice philosophy. The concept of pharmaceutical care was introduced as a further development of the pharmaceutical profession gaining acceptance in Europe and worldwide [48, 49]. The American Society of Health System Pharmacists set up guidelines for standardised pharmaceutical care to ensure that pharmacists practicing pharmaceutical care work to the same standard [50].
Patients with complex drug regimens and/or chronic diseases and those who frequently need to be hospitalised might benefit from pharmaceutical care in particular. These criteria apply to many cancer patients. Within the pharmaceutical care process the application of agreed therapeutic algorithms can be assured on the individual basis. The adherence of the patient can be improved by patient education before and during the treatment cycles combined with patient counselling regarding drug therapy, adverse effects and complementary treatment options.
The London oncology pharmacy group introduced guidelines for pharmaceutical care of cancer patients which not only include the actual ‘pharmaceutical care’ as such, but standardise the clinical pharmacy activities, dispensing, updating therapeutic policies, cytotoxic reconstitution, drug information, clinical trials, and the oncology training of pharmacists [51]. A group of British experts has drawn up a policy framework for commissioning cancer services. They suggest the establishment of structures which support the seamless care of cancer patients in the community setting in a network of all parties in order to make use of the respective specialist knowledge [52]. Accordingly, information flow at discharge from hospital to the community should be optimised utilising pharmaceutical care plans to ensure the efficient distribution of medication to the patient is not interrupted.
Although there are some reports on the implementation of pharmaceutical care for cancer patients there is still little scientific evidence on the feasibility of pharmaceutical care and its actual benefit to the patient. In Canada, projects have been carried out which suggest to implement suitable outcome parameters to evaluate the impact of pharmaceutical services in oncology [53]. These have stimulated a wide discussion in Canadian health care politics regarding the necessity of the services offered. In Scotland, research is concentrating on the documentation and thereby standardisation of the provision of pharmaceutical care to cancer patients [54]. In Germany, various research projects are being carried out which investigate the feasibility and benefit of pharmaceutical care for patients with different indications. Patients with breast and colorectal cancer as well as patients receiving oral chemotherapy are the focus of projects at the University of Bonn. Another project is being carried out in Hamburg developing the provision of pharmaceutical care to lung cancer patients [2].
In terms of cancer many disciplines contribute to the care process. Thus, cross-profession and cross-sector cooperation is crucial in order to improve information flow. Over the last few years disease management programs (DMPs) have increasingly been introduced to cover the whole care process which begins with the early diagnosis of the disease. They aim at providing the optimal medical care by the implementation of evidence-based guidelines. Pharmaceutical care concepts seem to have the potential to support the goals of DMPs and could be easily integrated in programmes for cancer patients. However, it is necessary to document the impact of pharmaceutical care on patient outcomes in order to comply with the demand for transparency.
Conclusions
Systemic cancer therapy is particularly complex and hence associated with multiple risks for the patient as described above. Many of these risks are preventable by specific measures which can be taken by a particular health care provider or the patient. The pharmacist with his central position relating to drug dispensing and utilisation can contribute substantially by adding specific drug-related knowledge and offering patient-related services.
Pharmaceutical care is designed as a framework that integrates individual contributions of the pharmacist into the entire therapeutic path. Model projects are urgently needed to assess the clinical, humanistic and economic outcome of patient-oriented pharmaceutical services. The integration of pharmaceutical care into disease management programmes might facilitate efficient collaboration between all health care professionals and hence improve effectiveness and safety of systemic cancer therapy.

cancer
drug-related problems
medication errors
oncology
pharmaceutical care
chemotherapy