Teaching clinical pharmacology and therapeutics with an emphasis on the therapeutic reasoning of undergraduate medical students

Background The rational prescribing of drugs is an essential skill of medical doctors. Clinical pharmacologists play an important role in the development of these skills by teaching clinical pharmacology and therapeutics (CP&T) to undergraduate medical students. Although the approaches to teaching CP&T have undergone many changes over the last decennia, it is essential that the actual teaching of CP&T continues to be a major part of the undergraduate medical curriculum.
Introduction
The field of modern clinical pharmacology began to develop in the second half of the 19th century when physicians began to realise that agents such as heavy metals and plant extracts, then in use, more often made diseases worse instead of better [1]. In 1969, a study group on clinical pharmacology was convened by the World Health Organisation (WHO) in order to demarcate the scope of this relatively new discipline [2]. They concluded that the tasks of the clinical pharmacologist include: (1) research into the action of drugs in humans, (2) services such as providing information on drugs and (3) teaching clinical pharmacology and therapeutics (CP&T) to medical students, hospital staff and physicians [2]. While both research and services are important areas of clinical pharmacology, it is in their roles as teachers of CP&T to medical students that clinical pharmacologists have an extremely important effect on the development of rational prescribing by medical doctors. However, despite this importance, the undergraduate teaching of CP&T has not, in contrast to the two other functions of the clinical pharmacologist, achieved an international level of recognition [3, 4]. The lesser attention given to teaching CP&T is also reflected in the small number of scientific publications. For example, only about 20 articles on teaching CP&T were published in the European Journal of Clinical Pharmacology between 1980 and 2000.
In many medical schools, undergraduate students learn little about the therapeutic use of drugs and even after graduation, some doctors read little on the subject and rely too much on the promotional efforts and information from the pharmaceutical industry. As indicated by a survey conducted under the auspices of the WHO in 1989, in European medical schools, an average of only 28 h was devoted to teaching clinical pharmacology, even though over 100 h were devoted to pharmacology [5, 6]. However, the number of hours spent teaching CP&T is probably underestimated since clinical pharmacology and particularly therapeutics are taught implicitly during the clinical clerkships.
The European Association of Clinical Pharmacology and Therapeutics (EACPT) was founded in 1993 to promote the field of CP&T. One of the important objectives of the EACPT was to develop CP&T in Europe by improving and harmonising the teaching of the rational use of drugs at the undergraduate and postgraduate levels. An Education Subcommittee of the EACPT was subsequently established in 1997 to increase the amount of attention given to as well as the quality of teaching CP&T. The first task of this subcommittee was to identify the educational requirements of medical students in terms of CP&T. A positive consequence of the expanded attention for the teaching of CP&T has been the substantial increase of scientific publications on teaching CP&T during the past decade: there have been about 32 articles published between 2000 and 2007 in the European Journal of Clinical Pharmacology, which is twice as many as during the preceding two decades.
Unfortunately, despite the increasing amount of attention being given to the teaching of CP&T, CP&T educational programmes are still not optimal in many medical schools [7]. Nowadays, many graduates still feel insufficiently prepared to assume prescription responsibilities after graduation [7]. Therefore, it is important to improve the undergraduate teaching in CP&T.
In this review, we shall endeavour to indicate the essential aspects of teaching CP&T to undergraduate medical students. To this end, we first describe what is known about the general learning objectives in CP&T that medical students should master before graduation. Subsequently, we focus on an essential (in our opinion) core learning objective: ‘therapeutic decision-making’. We go on to describe how medical students and physicians arrive at a therapeutic decision and discuss the possibilities of teaching medical students rational pharmacotherapy. Finally, we recommend approaches for organising, presenting and assessing CP&T in the undergraduate medical curriculum.
From core learning objectives to competencies
In many curricula, the teaching in clinical disciplines is centred on symptoms and diagnosis, and little or no time is given to the principles of drug treatment. In the past, CP&T was only taught as short courses in medical schools in which information was presented in the form of dogmatic indications of which drug to use and when. In some medical schools, however, clinical pharmacologists participated in basic pharmacology courses, usually given early in the clinical part of the curriculum [4]. Notwithstanding, much of the postgraduate and continuing education of the practicing physician has been left to the persuasive methods of the pharmaceutical industry [1].
It was only in 1989 that the Council for Medical Student Education in Clinical Pharmacology and Therapeutics stressed the importance of defining a core curriculum for clinical pharmacology in the medical curriculum. One year later, Nierenberg developed a core curriculum for medical students in CP&T [9]. Based on consensus between 40 clinical pharmacology teachers, Nierenberg formulated a list of core knowledge, skills and attitudes that every medical student should master before graduation in order to be able to prescribe effectively and safely [8, 9].
Core knowledge in clinical pharmacology deals with the facts that are necessary to make rational and optimal therapeutic plans; it includes the principles needed to organise these facts into useful information and to recognise when essential facts are missing [5, 8]. Core skills are in many ways similar to core knowledge because knowledge must be learned and used as the medical student develops skill in practicing rational therapeutics. Core attitudes include attitudes about what constitutes valid information, what kind of information is likely to be in error and what new information must be continuously acquired from reliable sources.
Based on the learning objectives as formulated by Nierenberg, a questionnaire on core learning objectives was subsequently developed in the United Kingdom by Walley [10]. Senior academic clinical pharmacologists who were active in teaching CP&T in the UK or Ireland assessed the relative importance of each learning objective as an element of a core curriculum. This resulted in a list of core knowledge, skills and attitudes [11] that largely matched the learning objectives as formulated by Nierenberg [8].
In addition to the approach of Nierenberg and Walley to defining the core curriculum in CP&T, Orme described two other approaches to developing a core curriculum for CP&T [12]. First, based on the WHO concept of an ‘essential drug list‘, he developed two lists of drugs; one list consisted of approximately 120 essential drugs that students would be expected to know in detail, and a shorter list consisted of drugs that students would be expected to be familiar with but were not required to know in any detail. This list of essential drugs was then examined by the Education Subcommittee of the EACPT. The second approach to defining a core curriculum for CP&T was the disease-based approach. Three types of disease process were defined: (1) 67 diseases that were common and that the student must know how to manage; (2) 158 diseases that were less common but that students must be able to diagnose, after which the appropriate therapy could be found in the literature; (3) 36 diseases that were rare but that students should be aware of.
Defining a core curriculum for CP&T as presented by Nierenberg, Walley and Orme is important to defining the shape and boundary of the discipline and may be helpful as part of a medical student's examination. However, in addition to knowledge, physicians must also have prescribing skills in order to prescribe rationally.
Therefore, the WHO Action Programme on Essential Drugs has developed, in collaboration with the University of Groningen, a manual for undergraduate medical students on the principles of rational prescribing, the so-called Guide to Good Prescribing (GGP). This manual provides a normative model for therapeutic reasoning and prescribing and provides a six-step guide to the process of rational prescribing that will be necessary throughout the clinical career of the medical student (Table 1). All six steps are based on the core learning objectives, knowledge, skills and attitudes as formulated by Nierenberg and Walley and on positive experience in the Netherlands [13, 14].
Table 1World Health Organisation six-step model of rational prescribing [18]StepsDescriptionStep 1Define the patient’s problemStep 2Specify the therapeutic objectiveStep 3aChoose your standard treatment (P-drug)Step 3bVerify the suitability of your treatment (P-drug)Step 4Start the treatmentStep 5Give information, instructions and warningsStep 6Monitor (and stop?) treatment
In addition, the content is based on an observational study among general practitioners and clinicians in the Netherlands and on 10 years of experience with pharmacotherapy courses for medical students in Dutch medical faculties and abroad. The GGP has also been reviewed and examined by a large body of international experts in the teaching of pharmacotherapy. As reported by De Vries et al., a short interactive training course in pharmacotherapy, using the GGP, was evaluated in a controlled study among 219 undergraduate international medical students [15]. This study indicated that undergraduate students who used the GGP performed significantly better than students who did not used the GGP. In addition, students not only remembered how to solve old problems, but they could also apply their skills to new problems, a so-called transfer effect.
A new framework for the innovation of medical curricula has been recently introduced, called the CanMEDS. The CanMEDS framework describes aspects of competence related to seven roles of a clinical specialist: the role of Medical Expert, Communicator, Health Advocate, Collaborator, Manager, Scholar and Professional [16]. Despite the fact that many doctors agree with the importance of these aspects of competence and that many countries have already adopted the CanMEDS roles in their curricula, there is limited information on how these roles can be applied in an international context and in different specialities, such as CP&T. Therefore, before clinical pharmacology as a discipline can adopt the CanMEDS competencies, the core learning objectives as determined in the last decades will have to be translated into the CanMEDS competencies.
In conclusion, in order to improve the competency of future doctors to prescribe effectively and safely, several clinical pharmacologists have attempted to determine what every medical student should master before graduation. A common feature of all of the competencies formulated is that all students must acquire a knowledge of the clinical pharmacology of essential drugs and diseases and must master prescribing skills in order to become competent in the rational prescription of drugs.
Therapeutic decision-making
Although, as described previously, there are many essential therapeutic skills, the final choice of a (drug) treatment for a patient can be looked upon as a core skill in therapeutics. In order to find effective ways to teach our future doctors, it is important to explore how expert doctors arrive at this final therapeutic decision.
Therapeutic decision-making or therapeutic reasoning is, together with diagnostic reasoning, an important part of the process of clinical reasoning. Therapeutic reasoning can be defined as the step in clinical reasoning that pertains to the choice of therapy [17]. However, in contrast to the process of diagnostic reasoning, which has been investigated extensively, little is known about the process of therapeutic reasoning. To our knowledge, the first attempt to investigate therapeutic reasoning in real practice was a small observational study carried out in 1984 in the Netherlands. The results of this unpublished study constituted the basis for the development of the WHO six-step model [18]. Over 500 patient consultations by 25 general practitioners and 25 clinical specialists were observed and recorded. In addition, all doctors were interviewed about their therapeutic reasoning. Based on this study, it became evident that doctors generally based their choice of (drug) treatment on two steps: (1) doctors initially used treatment guidelines or drug formularies as a starting point; (2) this was followed by a verification of the suitability of this treatment for the individual patient and a modification of the choice of treatment if necessary (for example, in case of co-morbidity or co-medication). Moreover, it was also observed that the more experienced physicians knew more standard treatments by heart compared to less experienced physicians.
The first study that described the process of how therapeutic decisions are made in practice in greater detail was performed by Denig [19]. By interviewing 169 different general practitioners and 72 hospital physicians, she studied how doctors arrive at a choice of drug and what factors influence this drug choice [19, 20]. This study indicated that when a doctor is confronted with a diagnosis – for example, a patient with both essential hypertension and a renal disorder – he or she immediately thinks of a number of pharmacotherapeutic possibilities, referred to as the ‘evoked set’. Depending on the diagnosis, the ‘evoked set’ consists of 1.7–5 different pharmacotherapeutic options that are influenced by many factors, such as refresher courses, the literature, experience and advertisements from the pharmaceutical industry. The ultimate choice out of the ‘evoked set’ for an individual patient may be either ‘unreasoned’ (routine) or ‘reasoned’ (evaluation of the different options). However, how the final choice of treatment is made is still unknown.
In contrast to the scarcity of information available on therapeutic decision-making, much research has been done on diagnostic reasoning. When an experienced doctor is confronted with a patient with certain symptoms and signs, so-called illness scripts are called up from memory. Illness scripts contain clinically relevant information on diseases, their consequences, the context in which diseases develop, including the personal circumstances, and the experience of the doctor with previous patients. These scripts are generated by the frequent solving of diagnostic clinical problems [21, 22]. Based on recognition, experienced doctors are able to choose the right script for solving a specific diagnostic problem efficiently, particularly in routine cases [23].
In order to verify whether this is the right script for the individual patient, two types of diagnostic reasoning may be used – analytical and non-analytical. Analytical reasoning is characterised as a slow and relatively time-consuming process that is carried out consciously and systematically and, if possible, evidence-based. Less or inexperienced doctors, such as medical students, mostly use this type of reasoning, mainly because they do not possess the ability to call up so-called ‘illness scripts’. In contrast to analytical reasoning, non-analytical reasoning is carried out rapidly and subconsciously and is based on experience and pattern recognition. This type of reasoning is used especially by experienced doctors. However, when an experienced doctor is confronted with a complex patient case, he or she will also use analytical reasoning.
Based on the similarities between therapeutic and diagnostic reasoning, it is possible to construct a hypothetical model of therapeutic reasoning (Fig. 1) [24]. When the diagnosis has been determined, one or more treatment scripts will be called up from the memory. In order to determine the right treatment, an analytical or non-analytical process, or a combination of both, will start. This process is similar to the process of diagnostic reasoning. The chosen treatment and its effect will contribute to the modification of the existing treatment scripts or may result in a new treatment script.
Fig. 1Hypothetical model of therapeutic reasoning [24]
However, in order to validate this hypothetical model, further research should be performed, particularly in terms of how experienced doctors arrive at their choice of treatment. In addition, we need to find out how these experienced doctors differ from less-experienced doctors, such as medical students and interns. The answers to these questions will provide more insight into how we should teach therapeutics to students although it can already be stated that early clinical practice will support the development of treatment scripts by undergraduate medical students.
How to teach and learn therapeutics
Gaining knowledge and at the same time applying this knowledge in practice is essential for learning in general and, presumably, also for the development of treatment scripts by medical students. This so-called context-learning seems to be more effective in many ways than sequential learning, in which learning and applying knowledge is separated [22, 25, 26]. The positive effect can be explained by theories from cognitive psychology and medical problem-solving [22, 26–28]. These theories suggest that the way in which knowledge is stored in the brain is essential for its recall and application. Therefore, storing pharmacotherapeutic knowledge in combination with the situation in which this knowledge will be applied benefits the speed and quality with which the information is recalled [21, 22, 29].
Context-learning is defined as learning in a setting that is similar to the setting of the future profession [30]. Context-learning is based on four basic principles: setting, repetition, feedback and responsibility [30]. The setting in which therapeutics is taught or learned should be the same as or as similar as possible to the setting of the future profession; for medical students, this is the clinical setting, such as in primary health care, hospitals or nursing homes. This clinical setting gives students the opportunity to gain experience the same way doctors do, allowing them to generate networks of organised knowledge in their memory and to develop illness and treatment scripts [31]. Subsequently, students should be given the opportunity to repeat the therapeutic problem-solving process as much as possible. Repetition allows students to generate networks of organised knowledge in the brain. Frequent exposure to patients and pharmacotherapeutic problems gradually condense these networks into readily accessible therapeutic scripts. Furthermore, students should also receive feedback immediately after their performance to assure the condensation of correct therapeutic scripts. Argumentation and motivational feedback may be a rapid way to reveal the process of therapeutic thinking and its possible errors. Finally, students should be responsible for their own learning. It is the student's own responsibility to repair any lack of knowledge or skills discovered during their clinical work and feedback sessions.
Apart from the most extreme form of context-learning, which is the clinical setting, different variations with lower levels of concreteness of the context are possible, varying from role-playing sessions with standardised patients in a simulated practice setting to the solution of written patient problems in small working groups and patient demonstrations during lectures. However, there are many ways to improve the setting, such as by using real case histories instead of written patient problems or through the use of video materials, laboratory test results or roentgenograms.
One possible approach to organising a context-learning programme in therapeutics is described by Vollebregt et al. [32]. These authors described a context-learning programme consisting of weekly-organized, role-playing sessions in the form of consulting hours. The role-playing sessions consist of three phases: consultation, argumentation and feedback. First, a ‘student’ doctor must carry out three therapeutic consultations of 10 min each. Before the start of a consultation, the doctors are given a written patient case. Subsequently, the ‘student’ patient and ‘student’ assessor enter the consultation room, and the doctor has to choose and prescribe the (drug) treatment interactively with the patient. The second phase (argumentation) starts immediately after the consultations. In this phase, the doctor has to substantiate the chosen therapy. Finally, during the third phase, all students sit together and discuss the various (drug) treatments and the performance of the doctors, guided by a clinical pharmacologist.
In addition to teaching therapeutics, assessing students’ therapeutic knowledge and skills is an essential component of the medical curriculum. In 1990, Miller suggested a framework for clinical assessment (Fig. 2) [33]. According to this framework, students, residents or physicians must have knowledge in order to carry out the required professional functions effectively. Students must also know how to use the knowledge that they have accumulated. For example, they must develop the skill that is needed to acquire information from a variety of human and laboratory sources, to analyse and interpret data and, finally, to translate such findings into a rational diagnostic or therapeutic plan. When this quality is functionally adequate, it is defined as a competence. Furthermore, students must not only be able to demonstrate that they know and know how, but also to show how they do it when confronted with a patient, which is called performance. Finally, it is important what a graduate actually does when functioning independently in clinical practice. According to Miller, this action component of professional behaviour is the most difficult to measure.
Fig. 2‘Pyramid’ by Miller as an illustrative framework for discussing the assessment of clinical skills, competence and performance [33]
The Objective Structured Clinical Examination (OSCE) and structured clinical examinations in general are well-known and approved methods for assessing competence and performance [34]. As far as the OSCE is concerned, this has also been shown to be useful for assessing therapeutic competence. Nevertheless, it is important that the manner in which the students are assessed is as similar as possible to the approach used in teaching them.
In conclusion, based on theories as to how physicians arrive at their therapeutic decisions, context-learning seems to be an effective way to teach CP&T to medical students. It is obvious that the ideal situation, i.e. real practice with real patients, is not always attainable, but various suboptimal forms of context-learning and assessment can be applied.
Conclusions and recommendations
In this document, the learning objectives of teaching CP&T to undergraduate medical students have been described, with an emphasis on therapeutic decision-making. Based on current theories of cognitive psychology and medical education, we have also discussed context-learning as an effective approach to teaching medical students how to prescribe rationally.
Until recently, there were only two groups of people who were permitted to prescribe drugs – registered medical practitioners and registered dental practitioners. Since 2006, some nurses, pharmacists and physician assistants have also been able to prescribe drugs for medical conditions within their area of competence [35]. Consequently, given this increasing number of registered prescribers with different qualifications, it has become increasingly important to train prescribers sufficiently in how to choose and prescribe drugs rationally. In addition, most curricula are changing from discipline- and subject-based teaching to competence and integrated or problem-based learning, resulting in less visibility of CP&T. The importance of teaching CP&T is further supported by the fact that many graduates still feel insufficiently prepared to assume prescribing responsibilities after graduation [7]. Furthermore, many hospital admissions and even deaths are caused by possibly avoidable medication errors [36].
Clinical pharmacologists should play an important role in the development of prescribing skills by teaching CP&T to undergraduate medical students. It is recommended that they formulate a CP&T context-learning curriculum within the medical curriculum – of course, in collaboration with physicians. This curriculum must be based on the final learning objectives of the CP&T education programme in which the required level of therapeutic competence of medical graduates has been determined. In addition, the curriculum must fulfil the criteria of context-learning, i.e. gaining knowledge and skills simultaneously in a (simulated) clinical practice setting. Furthermore, for the sake of clarity, it is stressed that the CP&T curriculum should be a visible part of the medical curriculum and that students must know, from the beginning, the required level of competence when they graduate. Current medical students often do not recognise the various CP&T teaching activities since they are scattered through the medical curriculum.
A draft of a CP&T context-learning curriculum is presented in Fig. 3. At the start of the curriculum, the emphasis lies on gaining CP&T knowledge and simultaneously learning to apply this knowledge by training skills in therapeutics. During this phase, little attention is given to the prescription of drugs in clinical practice. In the following study years, as CP&T knowledge and therapeutic skills increase, increased emphasis is given to prescribing in clinical practice, while the acquisition of knowledge and skills diminish. An example of a more specific CP&T curriculum is presented in Box 1.
Fig. 3A design of a clinical pharmacology and therapeutics (CP&T) context-learning curriculum (adapted from HJM van Rossum)
In conclusion, effective undergraduate teaching of CP&T is essential to improve rational prescribing and will immunize students against factors that may induce irrational prescribing after graduation. Therefore, based on current knowledge about learning, cognitive psychology and research in therapeutic teaching, a CP&T curriculum should be a prominent part of the medical curriculum. The CP&T curriculum should also be linked to postgraduate and continuing education in order to maintain an optimal competence in rational prescribing after graduating. Finally, to achieve a CP&T curriculum, allies should be found because clinical pharmacologists can and should not work on this alone. Medical students are already allies; they are interested in clinical pharmacology and really want to learn how to prescribe drugs rationally. Physicians should also become allies, since they can provide the clinical context for teaching and can prevent students from copying the bad prescribing habits of some of the physicians that train medical students. Associate clinical pharmacologists must collectively determine the current state and perspectives of the undergraduate education of CP&T in Europe. To this end, a European research project, which is organised jointly by the EACPT and British Pharmacological Society, has recently been started.
Ultimately, clinical pharmacologists, students and physicians collectively may be able to convince the policy-makers, such as the faculty boards, of the need for a CP&T curriculum. Such a joint effort is truly necessary because, unfortunately, the following words, spoken by Miller in 1990, are, to a great extent, still valid: “It will not be easy to convince conservative medical faculties, reasonably comfortable with the current conventions that allow clinical impressions to substitute for systematic accumulation of behavioural evidence, that change (in teaching, ed.) is in order”.
Box 1. Illustration of a CP&T curriculum
Figure 4 shows the different learning activities and their mutual relations. The horizontal lines represent the three learning components: knowledge [upper line: basic pharmacology, clinical pharmacology, therapeutics (1) and therapeutics (2)], clinical practice (middle line: video patients, patient demonstrations, observation of consultations and prescribing drugs) and skills [lower line: prescription, drug formulary, prescribing (6-steps) and CP&T literature]. The vertical lines describe the simultaneous use of these three components in order to realise a context-learning curriculum.
Fig. 4Outline of a CP&T curriculum
Let us use the example of a clinical pharmacology department that provides a course of lectures in basic and clinical pharmacology during the first three years of a classical medical curriculum. The aim of the department is to expand its teaching activities and use the context-learning methodology as much as possible. Therefore, the lectures in basic and clinical pharmacology must be transformed according to the criteria of context-learning, i.e. by adding patient cases (clinical practice) and prescription writing (skills). For example, the lecture about ‘P-450-dependent oxidation’ is placed into the clinical context by presenting a video of a patient case illustrating a drug interaction as the result of a wrong drug choice (WHO step 3b). The lecture concludes by discussing the patient case according to the six-step approach, including how to write a new prescription [18]. The clinical pharmacology lectures in the second and third year can be transformed in a similar way by presenting real patients, followed by lectures on different classes of drugs. Simultaneously with the clinical pharmacology lectures, students must develop a personal drug formulary by, for example, using an E-learning programme [3]. In the fourth year, students start on their clinical clerkships. In collaboration with their clinical colleagues, the clinical pharmacology department introduces a therapeutic assignment during these clerkships. Students must observe several consultations by clinicians in a structured way according to the WHO six-step approach and discuss these in small groups during therapeutic sessions with a clinical pharmacologist and/or physician. Concurrently, students must follow a skills training programme in order to learn how to prescribe rationally (six steps). During the clinical clerkships in the fifth and sixth years, prescriptions written by the students are evaluated by a clinical pharmacologist in collaboration with a physician and pharmacist during therapeutic sessions. Finally, students must attend a course on how to evaluate CP&T literature and how they must apply this knowledge in practice.
It is evident that in an integrated or problem-based medical curriculum it will be difficult to organise lectures. However, an advantage of this type of curriculum is that teaching is already centred on patient cases. As a result, clinical pharmacologists can introduce learning tasks with respect to basic and clinical pharmacology and therapeutics. In such a curriculum, it will probably be easier to train students in the six-step approach in small group-teaching sessions and practice prescribing in a clinical setting.
Obviously, there are various possibilities for context-learning teaching activities, and the above-mentioned are just a few examples of these. More detailed information on how to determine learning objectives for therapeutics and on methods for teaching and assessing therapeutics can be found in the WHO Teacher’s Guide to Good Prescribing [37]. In addition, this guide also provides information on how to mobilise support for changing and implementing a CP&T curriculum and how to perform research in this field.

teaching
undergraduate
education
pharmacotherapy
clinical pharmacology & therapeutics


Towards a pathway definition of Parkinson’s disease: a complex disorder with links to cancer, diabetes and inflammation

We have previously established a first whole genome transcriptomic profile of sporadic Parkinson’s disease (PD). After extensive brain tissue-based validation combined with cycles of iterative data analysis and by focusing on the most comparable cases of the cohort, we have refined our analysis and established a list of 892 highly dysregulated priority genes that are considered to form the core of the diseased Parkinsonian metabolic network. The substantia nigra pathways, now under scrutiny, contain more than 100 genes whose association with PD is known from the literature. Of those, more than 40 genes belong to the highly significantly dysregulated group identified in our dataset. Apart from the complete list of 892 priority genes, we present pathways revealing PD ‘hub’ as well as ‘peripheral’ network genes. The latter include Lewy body components or interact with known PD genes. Biological associations of PD with cancer, diabetes and inflammation are discussed and interactions of the priority genes with several drugs are provided. Our study illustrates the value of rigorous clinico-pathological correlation when analysing high-throughput data to make optimal use of the histopathological phenome, or morphonome which currently serves as the key diagnostic reference for most human diseases. The need for systematic human tissue banking, following the highest possible professional and ethical standard to enable sustainability, becomes evident.
Introduction
Our understanding of Parkinson’s disease (PD) is largely incomplete. However, the pace of discovery in this field is rapidly accelerating. It took more than 100 years for the key region of neuronal damage, the substantia nigra to be identified [1], and it took almost 80 years for the first disease-causing mutation to be discovered [2]. Not even 10 years later, the first whole genome transcriptome analysis had been performed [3], and a number of other microarray studies focusing on known sequences were carried out (e.g. [4–6]). We now provide the complete list of 892 highly dysregulated PD nigral genes derived from a brain tissue-validated whole genome expression microarray data set. In addition, predicted interactions of a number of these genes are reported as potential drug targets. We would like to emphasise that the neurohistological validation that is so crucial for our work and which has already led to the identification of two novel Lewy body components predicted on the basis of this dataset [7, 8] could not have been performed without generous brain donations. In addition, the iterative analysis performed combining histological phenome (morphonome) data and clinical criteria within silico data mining would not have been possible without significant advances in computing, notably virtual machine technology. It is readily apparent that a publication of this format requires the Internet as it would not have been possible to publish its almost 3,200 hyperlinked files on paper, which are provided as electronic supplemental material.
PD is a severely disabling neurodegenerative disorder second in frequency only to Alzheimer’s disease and has a significant socio-economic impact. Unlike in Alzheimer’s disease, however, the brain region taking the brunt of the disease process is rather well circumscribed. In addition, there is widespread consensus on diagnostic criteria both clinically and neuropathologically (http://www.ICDNS.org). This is at least in part due to the fact that the leading motor symptoms are less complex and easier to recognize and define than the clinical signs in disorders mainly affecting higher brain functions such as cognition. Furthermore, there is symptomatic treatment for PD pointing to key pathways involved. All these are important prerequisites when working with high throughput technologies such as microarrays which require precise tissue sampling because the procedures employed are both laborious and expensive.
Major known pathways involved in PD include the ubiquitin-proteasome system dysfunction of which may lead to abnormal protein deposition, mitochondrial failure and decreased expression of synaptic proteins [6, 9–11]. Oxidative stress has been traditionally implicated in the aetiology of the disease but the changes observed could be secondary. The concept of ‘neuroinflammation’ has become very popular recently [12–14], but our own work in this field does not currently support a role for microgliosis as a driver of the disease process [15]. The effective failure of recent studies employing non-steroidal anti-inflammatory drugs supports this notion [16, 17]. Thus, there are leads and popular ideas but the big picture of PD pathogenesis is still missing. A true understanding of PD and its subtypes will require integrated knowledge from several system biological levels, ranging from genomics to proteomics and metabonomics as well as clinical data and neuroimaging. Through this study, we aim to contribute a validated transcriptomic data layer.
Materials and methods
Data set used
The 94.CEL files used for this study have been deposited at the National Center for Biotechnology Information, Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/projects/geo) with GEO Series accession number GSE8397 (scheduled release date: 1 January 2008). The patient samples employed have been described previously [3]. This data set is based on Affymetrix HU_133A and HU_133B gene chips set and has been extensively validated over a period of 2 years using qRT-PCR, immunocytochemistry and in situ hybridisation to cellularly ‘back-map’ sequences of interest [7, 8, 10, 18].
Data analysis procedures employed
In silico analyses were performed with the help of programme packages from different suppliers. Our initial microarray data analysis was performed using ArrayAssist 3.0 (Stratagene) but there were intermittent problems with some versions of the software (3.2–3.4). We have repeated our analysis using newer (4.0, 4.1; Linux and/or Windows) and the latest version of this program (ArrayAssist 5.5 for Windows, Stratagene). In addition, we have reproduced our results using an independent software package for microarray analysis, PathwayStudio 5.0. The (GC-)RMA algorithm which has become a gold standard for Affymetrix microarray normalisation was applied in all cases. In addition, PathwayAssist (Ariadne) and Pathway Architect (Stratagene) were employed during phases of our work. For ease of use, most software installations were performed in virtual machines (VMware Workstation and Fusion, respectively; http://www.vmware.com) and two virtual machines were frequently run in parallel on Windows, Linux, or Macintosh platforms. The ability to create virtual machine ‘snap shots’ of critical stages of the analysis proved invaluable for the backtracking of results and to allow comparability over time. It proved essential in the case of the most complex network analyses where software stability proved to be a factor.
We have used the microarray data in a hypothesis generating rather than hypothesis testing way (cf. [4, 10]). Since our original predictions based on the ArrayAssist 3.0 dataset turned out to be very reliable with respect to subsequent in situ tissue validation results, we have performed our refined analysis by means of this programme following extensive comparison with readings generated by the latest versions of ArrayAssist 5.5 and PathwayStudio 5.0. Anyone intending to reproduce our results is referred to the original CEL files (GEO ID GSE8397).
The following original cases were excluded from our refined analysis to obtain more homogeneous cohorts taking newly obtained histological and expression data into account: Con4, 8 and MS155 as well as the sample of medial substantia nigra from PD22. Thus, nine control nigra and 23 PD nigra samples remained in the study. To find differentially expressed genes, the p value cut off was kept at 0.001 (differential expression = 1 log2). Multiple testing corrections (FDR, Bonferroni) were carried out for comparative purposes. The 892 top genes identified on the basis of 1,145 probes are referred to as the ‘priority genes’.
Hierarchical clustering was executed on both rows and columns using ArrayAssist 5.5 (Pearson centred distance metric, centroid linkage rule). Similarity images were produced to visualise the quality of clustering results (SI_Figure_1). In addition, self-organising map clustering was performed on both rows and columns using a Euclidean distance metric in ArrayAssist 5.5 (maximum number of iterations 50, number of grid rows 3, number of grid columns 4, initial learning rate 0.03, initial neighbourhood radius 5, grid topology hexagonal, neighbourhood type bubble).
For finding cell processes regulated by the differentially expressed genes, PathwayStudio software (version 5.0) was used. The 164 top up-regulated priority genes showing a differential expression >1 log2 were selected and the find common targets algorithm was employed in the build pathway tool setting the cell process filter option. The procedure was repeated for diseases entity type. Cell processes and disease conditions showing the highest number of biological associations, i.e. the strongest probabilistic relationship based on literature evidence to the group of 164 top up-regulated priority genes were selected. Furthermore, known interactions between all 892 dysregulated genes were identified using the ResNet 5.0 database of molecular interactions which has been derived from the published literature by means of a natural language processing technology called Medscan [19]. A filter for the parameters, promoter binding, expression and regulation was applied in the latter case.
For additional validation, the commercial PathArt database (PathwayAssist plug-in, Jubilant Biosys) containing a large number of manually curated pathways was queried. A number of GEO datasets (e.g. astrocytes and microglia in culture) were downloaded to evaluate cell-type specificity of expression of individual priority genes. A search for novel secreted biomarkers was also performed of which NPTX2 [8] may be one.
To retrieve known drug interactions of the 892 priority genes, more than 9,000,000 database objects were checked in ResNet 5.0 (PathwayStudio 5.0). Subsequently, all known interactions of individual relevant drugs contained in ResNet 5.0 were retrieved and the results overlaid with the set of priority genes. Drugs scoring high in terms of the number of their known interactions with the 892 priority genes were identified and the relationship to PD analysed employing the PubMed database (http://www.ncbi.nlm.nih.gov/sites/entrez).
Results
The extended set of 892 PD priority genes
Screening of histological and clinical parameters combined with iterative cycles of data mining enabled us to identify the most comparable cases in the cohorts which are detailed in SI_Figure_1. The number of highly significantly dysregulated genes (p < 0.001) increased from 429 (570 probes) to 892 (1,145 probes) while 97% of the originally identified genes [3] were confirmed. The complete list of all 892 PD priority genes is provided in SI_Table_1a. It should be mentioned that there are still some sequences that show highly significant dysregulation in PD on which there is no information available from ResNet 5.0, the IDconverter database [20] or NetAffx. However, all 1,145 probes (SI_Tables_1b&c) were used for the clustering (Fig. 1 and SI_Figure_1). Information on the 1,145 Affymetrix probes available from the public IDconverter database is provided in SI_Table_1d. SI_Table_1e shows the probe information not found in IDConverter or the ResNet database, respectively, that was obtained via NetAffx (http://www.affymetrix.com/analysis/index.affx). Thirty-one priority genes have been previously designated PD candidate genes (http://www.pdgene.org/) (SI_Table_1f), and it is worth noting that 33 are considered AD candidate genes as well (SI_Table_1g) with HSPA1A, HSPA1B, HTR2A, KCNJ6, SLC6A3, SNCA, SNCG, TF and UCHL1 being present in both lists. A PubMed search revealed that another 10 priority genes have known functional links to PD (SI_Table_1h). Finally, ‘whole genome/whole human body’ clustering using an independent data set derived from 64 different organ samples which is based on Affymetrix Human Genome U133 Plus 2.0 arrays allowed categorical separation of all nervous tissue samples including three different ganglia (SI_Figure_2).
Fig. 1Dendrogram showing the results of unsupervised hierarchical clustering of the 1,145 probes encompassing the 892 priority genes (corresponding Affymetrix probe IDs and expression data are provided online in SI_Figure_1). The algorithm separates the control (left) from the Parkinson’s disease group (right) (rows: red, expression above average; black, average expression; green, below average). An explanation of sample designations, patient data and a quantitative colour code are also provided in SI_Figure_1
Gene ontology and pathway analysis of the differentially expressed genes
The statistically most significant overlap (Fisher Exact test) between the 892 priority genes and GO groups was found for GO ID 0016020 (membrane) and GO IDs 0005515/0045308 (protein binding or protein degradation tagging activity, respectively). Further details are provided in the supplemental material (SI_Table_2). The main cell processes predicted to be influenced by the top up-regulated priority genes (164 genes showing differential expression >1 log2) are illustrated in Fig. 2a. A hyperlinked version of this figure providing details on all genes and their interactions is available online (SI_Figure_3a). A search of 192 canonical pathways and 555 signalling pathways in PathwayStudio 5.0 yielded the RET_HSF1 signalling pathway which shares six priority genes as the highest ranking result (SI_Table_3, SI_Pathway_1).
Fig. 2a Cell processes predicted to be influenced by the top up-regulated priority genes (164 genes showing a differential expression >1 log2) based on in silico analysis employing the ResNet database (PathwayStudio 5.0, Ariadne). P and expression values of all 1,145 probes can be found in SI_Table_1c. An online version of this figure with hyperlinks is provided as SI_Figure_3a. b The three disease conditions showing the strongest biological association with the group of top up-regulated priority genes which may serve as drivers of the disease process underlying PD. In silico analysis was performed employing the ResNet database of molecular interactions (Pathway-Studio 5.0, Ariadne). An online version of this figure with hyperlinks is provided as SI_Figure_3b
Predicted interactions of a subset of the 892 priority genes are shown in Fig. 3. A total of 417 known connections were retrieved from ResNet. This figure is provided for orientation purposes. Hyperlinked permutations of this figure showing details of all genes and their interactions are available online (SI_Figure_4).
Fig. 3Hypothetical ‘super pathway’ not stratified for cell type illustrating known direct interactions between the 892 PD priority genes (regulation, expression and promoter binding only). A total of 417 interactions (relations) are shown and any unlinked entities were removed. Display style: by effect; cellular layout; colour codes: promoter binding, violet; green, positive regulation; red, negative regulation; grey and/or broken lines, unknown (Resnet 5.0 database, unedited). Blue shading around selected genes indicates their involvement in the cellular process and disease conditions depicted in Fig. 2a and b, respectively. Permutations of this figure with hyperlinks are provided online as SI_Figures_4a-c
Known components of Lewy bodies and how they relate to the group of priority genes are presented in Fig. 4. The probes for most of these components were found to cluster together in the self-organising map shown in SI_Figure_5. A hyperlinked version of Fig. 4 with expression values overlaid is available online (SI_Figure_6). Interactions of known PD genes with the priority genes of this study are demonstrated in Fig. 5 (SI_Figure_7).
Fig. 4Neuronal pathway containing proteins found in Lewy bodies [11]. Priority genes of this study are marked by the blue shading. An online version of this figure with hyperlinks and an overlay of expression values is provided as SI_Figure_6. Symmetrical layout, display by effect (PathwayStudio)Fig. 5Interactions of known PD genes (marked yellow) with the priority genes of this study. Expression value overlay: red indicates up- regulation and blue indicates down-regulation in PD nigra. An online version of this figure with hyperlinks and an overlay of significance values is provided as SI_Figure_7. Cellular layout, display by effect (ResNet)
We did not observe an effect for gender (cf. [21]). Hierarchical clustering of the male and female PD patients on the basis of the expression values of the 892 priority genes did not separate the groups nor did a whole genome clustering omitting sex chromosomal sequences.
Relationship to diseases and drug interactions
A search of the ResNet database identified three disease conditions that showed the strongest probabilistic relationship to the group of top up-regulated priority genes, cancer, diabetes and inflammation as illustrated in Fig. 2b. A hyperlinked version of this figure providing details on all genes and their interactions is available online (SI_Figure_3b). Known drug interactions of some of the priority genes were retrieved from the ResNet 5.0 database through checking of more than 9,000,000 database objects. It is noteworthy that drugs such as clozapine, cocaine and haloperidol, which are used in the treatment of PD or which cause Parkinsonian side effects, appear to interact with a large number of PD priority genes (SI_Figure_8). A search of the ResNet database also yielded information on the interactions of two cytostatic drugs, paclitaxel and vincristine with the priority genes identified in this study (SI_Pathways_2&3). Both paclitaxel and vincristine have been reported to induce parkinsonian side effects [22, 23].
‘Hub’ vs ‘peripheral’ genes
A number of genes known to have numerous interactions with other genes were found amongst the priority genes. These represent so-called network ‘hubs’ and include HSPA1A, NFKBIA, CDC42, GSK3B, ACHE, AGTR1, IGF1R and TH as well as about 200 others (50 to >1,600 connectivities). Figure 2a and b contain a number of hubs. However, almost 30% of the priority genes have no known interactions according to the ResNet 5.0 database although the cellular localisation is known in some instances. Such genes may be called ‘peripheral’ genes of the human interactome [24, 25]. Apparently, peripheral priority genes with still unknown pathway connections and cellular localisation are ACTR10, ANKRD29, ANKRD34, ANKRD50, ARMCX4, ARRDC2, ASMTL, ATAD1, BLOC1S2, CAP2, CCDC4, CCDC85A, CKMT1A, CNIH3, DBNDD1, DCUN1D4, DIRAS2, DOPEY1, EHBP1, ELMOD1, FARSLB, FBXO9, FHOD3, GABARAPL3, GARNL4, GPRASP2, GPRIN3, GUSBP1, HISPPD1, HNRPUL2, IPW, KLHL1, KRT222P, LRRC49, LRRC55, LYNX1, MANEAL, MAP1LC3A, MAP9, MGC22265, MGC39606, MGC4677, MIA3, MRC1L1, NCDN, NGFRAP1L1, NRIP3, NUDT11, OCIAD2, OGDHL, OSBPL10, PCYOX1L, PFAAP5, PGM2L1, PLCXD3, PNMA6A, PRKY, PRMT8, PRPS1, RFPL1S, RUTBC2, SLC35F1, SNX10, SNX25, TAGLN3, TBC1D24, TBC1D9, TMEM130, TMEM132B, TMEM35, TRIM4, TRIM9, TSGA14, TTC7B, TUBB2B, TUBB3, UBPH, USP34, WDR47, XKR4, ZNF204.
Up-regulated peripheral genes are shown in Table 1. Following the above criteria, a number of Lewy body components and genes known to interact with the established PD genes seem peripheral to main brain metabolic pathways but their exact status remains to be determined.
Table 1Up-regulated ‘peripheral’ priority genes
Discussion
Our study reveals a significant up-regulation of substantia nigra genes in PD which have known biological associations with cancer, diabetes and inflammation. This includes major ‘hub’ genes [24–26] such as p53, somatic mutations of which can cause cancer. This is of note as p53 forms part of a molecular network that integrates tumour suppression and ageing [27]. DJ-1 is another cancer- and Parkinson’s disease-associated protein [28], and it is of special interest in the present context that the ubiquitin-proteasomal pathway has an established role in neoplastic processes [29]. Furthermore, both parkin and PINK1 might be tumour suppressor genes, and it has been suggested that although cancer is rare in PD, unraveling the link between PD and cancer [30–31] may open a therapeutic window for both diseases [32]. The finding of a molecular biological association between diabetes and PD is not truly surprising either [33–37]. Thirdly, the link of PD with inflammation which emerges from our unsupervised analysis seems almost expected considering the very lively debate of this topic in the literature. The whole genome transcriptome data presented here certainly justify additional scrutiny of the underlying mechanisms in relation to PD pathogenesis.
The problem of defining what causes PD at a system level has become more complex with the recent finding that disease-relevant genes may reside at the periphery of disease networks. It is interesting to note that the neighbour of a disease node appears more likely to be another disease protein, which also preferentially interacts with other disease nodes [38]. Proteins that are associated with the same disease show a 10-fold increased tendency to interact with each other than those not associated with the same disease [26]. This should direct our attention also to genes that do not form major network hubs but which are either likely to be involved in PD on cell biological grounds (e.g. Lewy body components) or which interact with PD causing genes. A significant fraction of the genes identified in this study still represent functionally ill-characterised entities.
The on-line material of this manuscript illustrates some of the pathways and biological association networks that emerge from our analysis. Networks are now recognized to pervade all aspects of human biology and the question where function lies within a cell is shifting from a simple focus on genes to the understanding that behind each cellular function there is a discernible network module consisting of genes, transcription factors, RNAs, enzymes and metabolites [39]. However, ‘network medicine’ is still in its infancy and the present study may be the first where an iterative multidimensional tissue analysis approach, http://www.neurogenetics.net/Multidimensional.html, has been applied to a human neurological disorder. The ultimate goal of such analyses is the precise cellular localisation of all expressed human disease genes in their affected tissues. The present PD dataset has so far yielded two novel components of Lewy bodies [7, 8] but much more back-mapping work will need to be performed.
For instance, the exact mechanism of cell death in PD is still unknown [40]. Recent evidence has suggested that one mechanism linked to the death of terminally differentiated neurons is aberrant re-entry into the cell cycle, and possible connections between oxidative stress and unscheduled cell cycle re-entry in PD have been proposed [41]. However, as neuroscientists, we may have to move beyond the description of the cell cycle that has been propagated by those in the cancer field because the regulation of the cell cycle in the neuron is much more nuanced (K Herrup, http://www.alzforum.org/new/detailprint.asp?id=1688). This raises the possibility that some of the data supporting cell processes such as mutagenesis in this study may have to be re-read and interpreted in a modified way. It is worth noting in this context that absence of RET signalling in mice causes progressive and late degeneration of the nigrostriatal system [42]. We would also like to point out that the present study provides additional evidence for the importance of changes in the neuronal cytoskeleton in PD [43–44] because neurofilament subunit as well as microtubuli-associated protein genes were found to be highly dysregulated. Dysregulation of signal transduction, heat shock and synaptic proteins also featured very prominently.
The view that the 892 nigral genes presented here are relevant for sporadic PD is supported by the finding that their pattern of expression is characteristic of nervous tissue (SI_Figure_2). It is further clear from our data that the human substantia nigra in PD does not represent dead tissue but that there is an active ongoing disease process understanding of which may hold the key to halting PD. The dysregulated priority genes may reside at the core of the disease process and could serve as novel targets for therapeutic intervention. This idea is supported by the observation that a number of priority genes interact with drugs whose actions are associated with a Parkinsonian clinical phenotype.
The uncertainty whether inflammatory processes truly represent a causative factor in the aetiology of PD [45] requires an answer. Our own work and that of others suggests a direct role of primary glial degeneration in the pathogenetic process underlying PD [10, 46]. This means that PD extends beyond the neuron. The disease is also not confined to the substantia nigra anatomically. Detailed cellular back-mapping of all priority genes to brain tissue will help to settle these questions. New algorithms are required to explain the links of PD as defined in the living and in the microscope with the underlying high-throughput datasets.
Collection of links to Electronic Supplemental Material (ESM)
SI_Table_1a List of all 892 highly dysregulated genes (‘priority genes’) contained in the ResNet 5.0 database (Ariadne) http://www.morphonom.net/ng/ESM/t/SI_Table_1a.xls
SI_Table_1b List of the 1,145 Affymetrix probes identifying highly dysregulated sequences including all 892 ‘priority genes’ http://www.morphonom.net/ng/ESM/t/SI_Table_1b.txt
SI_Table_1c P and differential expression values for the 1,145 Affymetrix probes http://www.morphonom.net/ng/ESM/t/SI_Table_1c.xls
SI_Table_1d Publicly available information on the 1,145 Affymetrix probes from IDconverter (http://idconverter.bioinfo.cnio.es/) http://www.morphonom.net/ng/ESM/t/SI_Table_1d.xls
SI_Table_1e Information on gene sequences not found in the ResNet database obtained via NetAffx (http://www.affymetrix.com/analysis/index.affx) http://www.morphonom.net/ng/ESM/t/SI_Table_1e.xls
SI_Table_1f List of priority genes that are published PD candidate genes (http://www.pdgene.org/) http://www.morphonom.net/ng/ESM/t/SI_Table_1f.xls
SI_Table_1g List of priority genes that are published AD candidate genes http://www.morphonom.net/ng/ESM/t/SI_Table_1g.rtf
SI_Table_1h List of priority genes with known functional links to PD (PubMed) http://www.morphonom.net/ng/ESM/t/SI_Table_1h.xls
The literature references for this table can be found in http://www.morphonom.net/ng/ESM/r/SI_References_1.rtf
SI_Table_2 Gene Ontology analysis of the 892 genes http://www.morphonom.net/ng/ESM/t/SI_Table_2.xls
SI_Table_3 Signalling pathway analysis of the 892 genes (PathwayStudio 5.0) http://www.morphonom.net/ng/ESM/t/SI_Table_3.xls
SI_Figures_1a Hierarchical clustering of the 1,145 probes (dendrogram description: clustering on rows and columns, Pearson centred distance metric, centroid linkage rule); the clustering separates PD cases (green, right) from controls (red, left); designation of samples as indicated in the dataset submitted to GEO (see ‘Materials and methods’ section). http://www.morphonom.net/ng/ESM/f/SI_Figure_1a.png
Abbreviations used: F, female; M, male; LN, lateral substantia nigra; MN, medial substantia nigra; CON, PDC, controls; PD, Parkinson’s disease; followed by the number indicating the age of each subject (cf. Moran et al. [3]); LN, MNCON10, M, 71; LN, MNCON2, M, 77; MNCON3, M, 81; LN, MNCON9, M, 57; LN, MNPD01, F, 87; LN, MNPD02, M, 83; LN, MNPD04, M, 68; LN, MNPD07, M, 78; LN, MNPD09, F, 86; LN, MNPD10, F, 81; LN, MNPD16, F, 85; MNPD20, M, 75; MNPD21, M, 76; LN, MNPD28, M, 82; LN, MNPD29, M, 76; MNPD32, M, 89; MNPD34, F, 84; MNPD36, M, 76; LN, MNPDC1, M, 76.
An explanation for the colour coding is provided in http://www.morphonom.net/ng/ESM/f/SI_Figures_1a&c-Color_range.jpg
The labels used for the clustering and the corresponding Affymetrix probe set IDs are explained in http://www.morphonom.net/ng/ESM/f/SI_Figures_1a&c-Labels_used_for_clustering.xls
The four probes at the very bottom of the figure (XIST, X (inactive)-specific transcript) in PD01, 9, 10, 16 and 34 identify the female patients in our refined cohort. They served as an internal control.
SI_Figures_1b Column similarity image for SI_Figure_1a.png http://www.morphonom.net/ng/ESM/f/SI_Figure_1b.png
SI_Figures_1c Self-organising map corresponding to SI_Figure_1a (dendrogram description: clustering on rows and columns, Euclidean distance metric, maximum number of iterations 50, number of grid rows 3, number of grid columns 4, initial learning rate 0.03, initial neighbourhood radius 5, grid topology hexagonal, neighbourhood type bubble) http://www.morphonom.net/ng/ESM/f/SI_Figure_1c.png
SI_Figure_2 Hierarchical clustering of the 1,145 probes using 64 whole genome array datasets (Affymetrix Human Genome U133 Plus 2.0 arrays) representing individual organ samples including 20 brain regions and three ganglia [47]. GSM numbers refer to the respective file names in the complete dataset which comprises 353 whole genome arrays (GEO database, GSE ID GSE3526). There is a complete separation of nervous tissue (left) from other organs on the basis of the 1,145 probes. http://www.morphonom.net/ng/ESM/f/SI_Figure_2.png
The designations of all samples and their code numbers are provided in http://www.morphonom.net/ng/ESM/r/SI_References_2.rtf
SI_Figure_3a Online version (with links) of Fig. 2a http://www.morphonom.net/ng/ESM/f/Cell_Processes.html
SI_Figure_3b Online version (with links) of Fig. 2b. (XIST was not included in this analysis). http://www.morphonom.net/ng/ESM/f/Disease.html
SI_Figures_4a-c Online permutations of Fig. 3
Layout by cellular localisation with links http://www.morphonom.net/ng/ESM/f/892.html
Symmetrical layout with links http://www.morphonom.net/ng/ESM/f/892s.html
Hierarchical layout with links http://www.morphonom.net/ng/ESM/f/892h.html
SI_Figure_5 Known components of Lewy bodies are indicated by the blue highlighting (this figure is identical to SI_Figure_1c except that it has a lower resolution) http://www.morphonom.net/ng/ESM/f/SI_Figure_5.png
SI_Figure_6 Online version of Fig. 4 with expression values overlaid (red indicates up- and blue indicates down-regulation in PD nigra, respectively). The blue shading of the priority genes has been replaced with yellow. http://www.morphonom.net/ng/ESM/f/Lewy_Body.html
SI_Figure_7 Hyperlinked online version of Fig. 5. The gene colours range in this figure indicates significance: high, white; low, red. The blue shading indicates that the respective gene is a priority gene. http://www.morphonom.net/ng/ESM/f/PD_genes_interactions_with_892_direct_no_DorCP.html
SI_Figures_8a-c Known drug interactions of some of the priority genes as derived from the ResNet 5.0 database (more than 9,000,000 database objects were checked). It is noteworthy that drugs such as clozapine, cocaine and haloperidol which are used in the treatment of PD or which cause Parkinsonian side effects appear to interact with an especially large number of PD priority genes.
Clozapine http://www.morphonom.net/ng/ESM/f/SI_Figure_8a.png
Cocaine http://www.morphonom.net/ng/ESM/f/SI_Figure_8b.png
Haloperidol http://www.morphonom.net/ng/ESM/f/SI_Figure_8c.png
SI_Pathway_1 This signalling pathway was identified based on a search of 192 canonical pathways and 555 signalling pathways in PathwayStudio 5.0 and ranked highest (also see SI_Table_3). Six priority genes are represented in this pathway and are marked by the blue shading. http://www.morphonom.net/ng/ESM/p/RET_HSF1_signaling_pathway.html
SI_Pathway_2 Interactions of the cytostatic drug, paclitaxel with a total of 13 priority genes (blue shading) are shown. Display by effect. Hierarchical layout. http://www.morphonom.net/ng/ESM/p/paclitaxel_interactions.html
SI_Pathway_3 Interactions of the cytostatic drug, vincristine with 3 priority genes (violet shading, bottom of figure) are illustrated. Red shaded genes also showed dysregulation in the PD nigra (p < 0.001). Display by references count (darker blue indicates a larger number of references supporting the respective connection). Hierarchical layout. http://www.morphonom.net/ng/ESM/p/vincristine_interactions.html
SI_References_1 References for SI_Table_1h http://www.morphonom.net/ng/ESM/r/SI_References_1.rtf
SI_References_2 Designations and code numbers for SI_Figure_2 http://www.morphonom.net/ng/ESM/r/SI_References_2.rtf

lewy bodies
microarrays
network medicine
aposklesis
expression analysis
neurodegeneration
synaptic dysfunction


Medication administered to children from 0 to 7.5 years in the Avon Longitudinal Study of Parents and Children (ALSPAC)

Objective To present data on the parentally-reported use of all types of medicinal products in children from 0 to 7.5 years, in a large cohort in south-west England.
Introduction
It is now well recognised that there is a need for more studies to obtain paediatric information on medicines used in children with the aim of reducing the amount of unlicenced and off-label use of medicines in this population. In 2002, 15% of children under 5 and 20% of children aged 5–15 were reported as having a long-standing illness [13]. In addition, young children may experience a range of relatively minor ailments requiring medical treatment. A previous report using data from this cohort showed that cold, cough, high fever, colic/stomach ache, diarrhoea and vomiting were very common symptoms in the pre-school years, and that cough, cold, earache and high temperature are the most common symptoms to present to medical services [6]. In 2004, 35.7 million prescription items were dispensed in the community for children under 16 at a cost of £293.8 million [7]. This represents an average of 4.5 items per head in the age range 0–15 years [7]. The categories of drugs most frequently prescribed for children are antibacterials, analgesics and bronchodilators [12]. Prescribing outside the terms of the product licence occurs in drugs commonly prescribed in general practice such as systemic antibiotics [11].
Although children are entitled to free medicines on a National Health Service prescription in the UK, parents may also choose to purchase products over-the-counter (OTC) for various reasons, such as convenience and confusion over their entitlements [2]. In a US survey involving 8,145 3-year-olds, 54% had been given an OTC medication in the last 30 days, most commonly paracetamol and cough/cold medicines [8], and a Norwegian study concluded that paracetamol was an important tool for parents in managing their children’s illnesses [9]. A significant proportion of OTC medicines used in children may be purchased from non-pharmacy outlets, where advice on appropriate use is unavailable [3].
This study describes the patterns of parentally-reported use of all types of medicinal products in a large community sample of young children, at four data collection times between the ages of 4 weeks and 7.5 years.
Methods
The data for this study were collected as part of the Avon Longitudinal Study of Parents and Children (ALSPAC): a longitudinal study designed to investigate the health and development of children [5]. The study was approved by the ALSPAC law and ethics committee, and the three local research ethics committees.
Pregnant women resident in the three Bristol-based health districts in the former county of Avon in southwest England, and with an expected delivery date falling between 1 April 1991 and 31 December 1992, were eligible to be enrolled. The core ALSPAC sample consists of 14,212 women who had a total of 14,541 study pregnancies, involving 14,676 fetuses, resulting in 14,062 live births. An additional 548 children from 542 eligible pregnancies not included in the core sample were included in the study from age 7. Cohort retention has been good, and we are currently in regular contact with approximately 11,000 families. Further details can be obtained from the ALSPAC website, http://www.alspac.bris.ac.uk.
From various studies performed at the planning stage, the Avon population was considered to be broadly similar to that of the whole of Great Britain. After delivery, the representative nature of the sample was assessed. As in similar studies, the ALSPAC cohort has a slight shortfall in the less affluent families and a shortfall in ethnic minority mothers. Birth weights and lengths and growth measures in ALSPAC are very similar to national standards [5].
The primary source of data collection is via postal questionnaires about the health, development and home environment of the study children completed by each child’s main carer, usually the mother. This paper presents data obtained from four such questionnaires administered when the study children were 4 weeks, 54, 78 and 91 months old. Each of these asked questions, designed by the ALSPAC study team, about the use of medicinal products.
The 4-week questionnaire (sent out between 1991 and 1993), asked the baby’s mother to: “Please list all the ointments, pills and medicines that have been given to your baby while he/she has been at home. Check: have you included ointments to prevent nappy rash, eyedrops, herbal remedies, etc.”
The questionnaire sent out between 1995 and 1997, when the child was 54 months of age (4.5 years) asked the following:
Children often have accidents or illnesses that need treatment. Please indicate which of the following have been given to your child since he/she was 3 years old
Cough medicineAntibiotics/penicillinThroat medicineVitaminsParacetamol/calpolOintment for skinEye ointmentDiarrhoea mixture/pillsDimotapp/decongestantEar dropsEye dropsIronLaxativesHomeopathic medicineHerbal medicineOtherFor each category, the carer was asked to tick whether the response was ‘Never’, ‘Yes, for one episode only’ or ‘Yes, for 2 or more episodes’; and ‘If yes, please give full names of substances if you can’.Are there any pills, ointments or medicines that he/she has taken every day or nearly every day for the last 3 months? (Include vitamins, skin cream, inhaler, laxatives as well as antibiotics, homeopathic and herbal remedies, etc.) If yes, please describe.
At 78 months (6.5 years, data collection 1997–1999) and 91 months (7.5 years, data collection 1998–2000) the first question was expanded slightly as follows:
Children often have accidents or illnesses that need treatment. Please indicate which of the following have been given to your child in the last 12 months.
Cough medicineAntibiotics/penicillinThroat medicineVitaminsParacetamol/calpolOintment for skinEye ointmentDiarrhoea mixture/pillsDimotapp*/decongestantEar dropsEye dropsIronLaxativesHomeopathic medicineHerbal medicineAsthma medicationVaporiserOther
(*active ingredients: brompheniramine, phenylephrine and phenylpropanolamine)
The tick box response options, the request for full name of the product, and the second question were the same as at 54 months.
After a questionnaire was sent out, if a response was not received within 3 weeks, a reminder letter was sent. If the questionnaire had still not been received after a further 2 weeks, a second reminder letter was sent. Finally, if no response had been received after 3 months, a member of the study team either rang the mother or visited her home, and encouraged or helped her to complete the questionnaire. The date of completion is included on each questionnaire, so whilst all returned questionnaires are added to the database and included in the overall response rate, those that were completed after a defined cut-off date can be excluded from individual analyses.
Most of the self-completion responses on the questionnaires are self-coding—the ticked reply box contains a printed number that can be directly keyed. However, the actual names of medicinal products used (including vitamins and supplements, herbal products, homeopathic remedies, over-the-counter and prescription products) were collected as free text responses.
An ALSPAC drug dictionary has been developed based on the World Health Organisation (WHO) Drug Dictionary (first quarter 2000 version, provided free of charge by the Uppsala Monitoring Centre). The ALSPAC dictionary assigns a Drug Name and Anatomical–Therapeutic–Chemical (ATC) classification as described in the WHO Drug Dictionary to an individual product. The latter classification groups medications according to the organ or body system on which they are designed to act and their chemical, pharmacological and therapeutic properties. Some drugs have several ATC codes as they can be used in different preparations on different body systems. The ALSPAC dictionary codes preparations, and thus a single ATC, has been assigned to each product. In the few cases where more than one ATC code would have been possible, a decision was made as to the most likely use in this population. Additional details about the WHO Drug Dictionary can be found on the WHO website: http://www.whocc.no/atcddd/.
In cases where a category of medicinal product was ticked as having been used but no further details were provided, a non-specific code for that category has been added to the database of text responses.
For the purposes of this report, all responses indicating that a product in the defined category had been used have been included, thus indicating ‘ever use’ of these categories of medicinal products rather than taking into account frequency of use.
Results
The response rate was good: Table 1 gives the number of questionnaires mailed and returned at each age. The majority of questionnaires were completed at or close to the intended age. For example, 90% of the returned month 54 questionnaires had been completed by 55 months and 99% by 58 months, so all data from the 54–91 month questionnaires have been included. However, as the purpose of the question at 4 weeks was to ascertain what medicinal products were used on the study children as very young babies, a cut-off was applied so that only questionnaires completed before the child was 8 weeks old were included. Using this criterion, at least one questionnaire was completed for 12,525 study children and all 4 were completed for 6,635 children.
Table 1Total number of medicinal products recorded per child at each age 4 weeks (<8 weeks)54 months78 months91 monthsNo. of questionnaires sent out13,65912,37410,91411,251No. of completed questionnaires10,897a9,7258,5788,269Total no. products recorded per child02,57312933941013,0295571,0101,13922,5941,4001,7461,91131,5022,0201,7151,75946871,8341,3701,31753201,3821,0017716130939590451739578346227813352179133910205139771010351431174333212492412133013714256715161061619191710132181251900220212110220231aCompleted within 8 weeks of birth
For most of the defined categories, the number of tick box responses not supported with a text response giving the name of the product increased between the ages of 54 and 91 months (at 4 weeks, responses to the medication question were all collected as free text).
The week 4 questionnaire was completed before the child was 8 weeks of age in 10,897 cases (80% of those sent, 88% of those returned). Around three-quarters of study children were exposed to some form of medicinal product before 8 weeks of age. At this age, dermatological products were most commonly reported, mainly those for treatment and protection against nappy rash, such as ‘Sudocrem’, a proprietary product containing zinc oxide used in 29% of babies, and zinc and castor oil cream used in 22%. Simeticone (activated dimeticone) in products for treatment of colic and flatulence, such as ‘Infacol’, was given to 16% of babies. Gripe water (terpeneless dill seed oil and sodium bicarbonate) was used in 13% of the cohort. Other commonly reported products included oral and topical antifungals and ophthalmic antibiotics (mostly chloramphenicol). A few babies (2.3%) were given vitamin drops. Some were given herbal drinks (2.2%), homeopathic products (1.2%) and a variety of topical complementary and alternative medicine products, such as calendula and camomile creams (3.3%).
Several OTC products not licensed for use in this age group were reported. For example, inhalant decongestant products not licensed for children under 3 months of age—Snuffle-babe Vapour Rub (eucalyptus oil, menthol and thyme oil), Karvol Decongestant Capsules for inhalation (levomenthol with chlorobutanol, pine oils, terpineol and thymol) and Vicks Vaporub (levomenthol, camphor, eucalyptus oil and turpentine oil)— were used in over 6% of the cohort, and xylometazoline nasal spray was used on 0.6%. Calpol (paracetamol), which is licensed from 2 months, was given to over 1% when they were under 8 weeks old.
At the older ages, 54, 78 and 91 months, 9,728 (78% of those mailed), 8,578 (78%) and 8,269 (73%) questionnaires were completed and returned, respectively. In these latter questionnaires, relatively few children were reported not to have used any medicinal products in the preceding 12–18 months. At 54 months (4.5 years), only 129 children (1.3% of those returning the questionnaire) were not reported as having used a medicinal product since the age of 3. At 78 and 91 months, the figures were 339 (4.0%) and 329 (4.0%), respectively. Table 1 shows the number of products reported per child at each age.
Table 2 shows the frequency of use of medicinal products, by ATC code, at each data collection time. The data are presented as the total number of drugs reported in an ATC class per number of children in which any products within this class were used (i.e. more than one product in a class may have been reported per child). The pattern of use of different categories of medicinal product varied with age. Usage of several product categories was higher at 54 months than at 78 or 91 months. For example, at 54 months, 1,052 children (10.2%) were reported to have used topical steroids (mostly hydrocortisone) compared with 7.4% at 78 months and 4.9% at 91 months. Paracetamol (as a single ingredient) was recorded for 9,211 children (94.7%) at 54 months, 7,595 (88.5%) at 78 months and 7,247 (87.6%) at 91 months. In addition, systemic antibiotics (mainly amoxicillin), laxatives (mostly lactulose), treatments for diarrhoea (mainly rehydration salts), various cough and cold products, emollient and protective skin preparations (mostly paraffin-based proprietary products), topical antipruritics, antihistamines and local anaesthetic products were all greater at 54 months than at the older ages. Conversely, use of other categories, such as throat preparations (sprays and lozenges), and anti-inflammatory products, increased with increasing age. Use of ‘asthma’ medication (mainly salbutamol and beclomethasone) also increased with increasing age; at 54 months, 6.8% of the cohort reported use of anti-asthmatics, by 78 months this had increased to 12.0% and to 14.2% at 91 months. Of the 1,174 children reported as having used asthma medication at 91 months, 1,038 (88%) had previously had a diagnosis of ‘asthma’ from a doctor and 907 (77%) reported having asthma in the past 12 months.
Table 2Frequency of use of medicinal products, by ATC code, at each age. Total number of drugs reported in a class/number of children in which this class of product was used (individual children may have used more than one product in a class). ATC level 1 (in bold) includes all reported cases. ATC level 2 is given where 10 or more children reported a product in this category in at least one questionnaireATC code <8 weeks54 months 3–4.5 years78 months 5.5–6.5 years91 months 6.5–7.5 yearsNo. of questionnaires returned with data10,8979,7258,5788,269 ATC text    AGastrointestinal preparations4,470/3,4166,733/4,1884,747/3,1304,262/2,973A01Stomatological preparations691/649114/9457/4635/30A02Antacids, drugs for peptic ulcer and flatulence3,422/2,69031/2725/2315/13A03Antispasmodics, anticholinergics and propulsives29/2913/1213/119/9A06Laxatives41/41528/421277/200328/262A07Antidiarrhoeals, intestinal anti-inflammatories and anti-infectives39/38693/682196/191227/224A11Vitamins247/2475,169/3,5673,143/2,1213,748/2,692A12Mineral supplements1/1170/15540/27158/149BBlood and blood forming organs61/4757/5028/2132/25B03Antianaemic preparations46/3248/4128/2131/24CCardiovascular system13/116/58/74/3DDermatologicals9,618/6,3975,105/3,2693,538/2,6532,988/2,441DDermatologicals unspecified25/241,015/1,010915/9071,376/1,373D01Antifungals for dermatological use584/557173/163185/173125/118D02Emollients and protectives7,908/5,6551,954/1,45312,18/928690/519D04Antipruritics, including antihistamines, anaesthetics 6/6158/15191/8959/57D06Antibiotics and chemotherapeutics, topical98/95169/161154/142107/99D07Corticosteroids, topical125/1211,358/1,052790/631507/408D08Antiseptics and disinfectants843/785246/223161/154114/110D11Other dermatological preparations25/2516/1411/113/3GGenito-urinary system and sex hormones1/117/1518/169/8HSystemic hormones, excl sex hormones3/369/5963/5649/40H01Pituitary, hypothalamic hormones and analogues02/212/814/11H02Corticosteroids for systemic use1/164/5548/4634/28JGeneral anti-infectives for systemic use354/3416,505/5,9763,322/3,1512,434/2,358J01Antibacterials for systemic use346/3356,498/5,9733,309/3,1462,419/2,349LAntineoplastic and immunomodulating agents001/12/1MMusculo-skeletal system063/62111/107119/118M01Anti-inflammatory and antirheumatic products062/61105/103117/116NNervous system163/1599,561/9,1847,857/7,6207,400/7,261N02Analgesics157/1549,510/9,1817,802/7,6167,344/7,256N03Anti-epileptics1/139/2838/2125/15N06Psychoanaleptics (antidepressants/stimulants)1/14/25/524/17PAntiparasitics, insecticides and repellents087/8090/8458/54P02Anthelmintics066/6562/5937/36P03Ectoparasiticides, scabicides, insecticides, repellents014/1322/2217/16RRespiratory system1,168/1,0329,609/6,9958,615/5,4927,796/5,131R01Nasal preparations468/456705/655437/391403/382R02Throat preparations0471/471514/511649/646R03Anti-asthmatics01,178/6582,422/1,0291,920/1,174 R05Cough and cold preparations697/6437,839/6,4345,058/4,6784,684/4,391 R06Antihistamines for systemic use3/3216/186184/163140/127SSensory organs1496/14112954/22131051/8841090/861 S01Ophthalmologicals1,316/1,2571,852/1,444536/438633/499 S02Otologicals3/3737/728426/416405/403 S03Ophthalmological and otological preparations177/177365/31489/8452/49VVarious855/7051,082/745955/7051,139/796 V03All other therapeutic products (including CAM)587/5041,079/744954/7041,138/795 V07All other non-therapeutic products (eg baby toiletries)295/21401/10
Treatments for less common conditions were also reported by this community sample. By 91 months, 6 children were using insulin, 17 reported methylphenidate (for attention deficit hyperactivity disorder) and 2 reported thyroid therapy. At 54 months, 28 children reported antiepileptic medication but this had reduced to 15 children by 91 months.
Between 54 and 91 months, a very small number of children were reported to have been given an OTC medicine, whilst they were younger than the age specified on the Patient Information Leaflet or Summary Product Characteristics, for example, aspirin, ‘Dequadin’ lozenges (dequalinium chloride), ‘Night Nurse Liquid’ (paracetamol, pomethazine hydrochloride, dextromethorphan hydrobromide) and ‘Diocalm’ (morphine hydrochloride and attapulgite).
Discussion
The advantage of collecting data on children’s use of medicinal products from their parents/carers, as opposed to from medical records and prescription databases, is that the former are in a position to know what was actually given to their children, rather than what may have been prescribed but not collected or taken by the child. In addition, the data potentially includes medication obtained from all sources—GP’s, pharmacies, supermarkets, CAM practitioners, friends, relatives and neighbours. However, potential issues include the accuracy of recall (especially over long periods of 12–18 months, as used at the older ages in this study) and parental knowledge and understanding about the medicines given to their child.
These data were collected over the period 1991–2000. Between 1994 and 1998, the annual number of children aged 5–15 years prescribed antibacterial drugs ranged from 322 to 402 per 1,000 [12]. This compares favourably with the current study where the number reporting use of antibiotics over the previous 12 months was 367 per 1,000 at 6.5 years of age. At 7.5 years, the rate was lower at 284 per 1,000, but these latter data reflect use of antibiotics between 1997 and 2000, when a substantial decline occurred in the prescribing of antibiotics by general practitioners, in an effort to curb the spread of antibiotic resistance [14]. Children under 5 are known to be the greatest users of antibiotics [10, 12], and the data from this study are consistent with this in that antibiotic use reported at 4.5 years was considerably greater than at 6.5 and 7.5 years.
The number of children aged 5–15 years prescribed bronchodilators over the period 1994–1998 ranged between 135 and 141 per 1,000 boys and 113–118 per 1,000 girls [12]. The prevalence of treated asthma in the same age group was 128–132 per 1,000 boys and 100–106 per 1,000 girls, and around 94% of these are prescribed bronchodilators [12]. In this study, the reported use of all ‘asthma’ medication for boys and girls combined was 120 per 1,000 at 6.5 years and 142 per 1,000 at 7.5 years. Thus, the figures from this study do not suggest a lack of recall. Indeed, at 7.5 years, the reported use of asthma medication is slightly higher than the national figures. However, as the latter combine data over a 10-year age range (5–15 years) any differences at individual ages may be masked.
Therefore, as the questionnaire data on use of prescribed antibiotics and anti-asthmatics in this study is in the range expected from prescription data, parentally-reported use of non-prescription medication in this cohort is also likely to be reasonably reliable.
The findings suggest that most children use some form of medication from a very young age. Medicines available over-the-counter were widely used, although it was not possible to determine from these data whether they were actually purchased over-the-counter or prescribed. The reason for use was not collected, so it was also not possible to assess to what extent products were appropriately used. For example, paracetamol has been found to be used by parents to calm and settle their children and aid sleep [1, 9]. Some products were used in children younger than the age for which they were licensed. It was not possible to assess general adherence to information given in the patient information leaflets or whether advice was sought prior to using a product outside the specifications, but parents of young children should be encouraged to follow the labelling advice or seek advice from a health professional.
The ALSPAC database provides a resource to investigate the use of commonly used medications with later health and development; for example paracetamol, which is widely used and may be associated with development of asthma [4]. Future work may also examine differences in usage by sex of the child and socio-economic status of the parents.

medication
children
alspac
paediatric drug use