Chronic Pain

Chronic pain

From the study of student attitudes and preferences to the in vitro investigation of a novel treatment strategy

Linda Rankin

Department of Integrative Medical Biology Umeå 2020 This work is protected by the Swedish Copyright Legislation (Act 1960:729) Dissertation for PhD ISBN print: 978-91-7855-267-2 ISBN PDF: 978-91-7855-268-9 ISSN: 0346-6612 New Series Number 2085 Cover layout: Inhousebyrån Electronic version available at: http://umu.diva-portal.org/ Printed by: CityPrint I Norr AB Umeå, Sweden 2020

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Table of Contents

Original papers ...... iiii Abstract ...... iv Abbreviations ...... v Enkel sammanfattning på Svenska ...... vii Introduction ...... 1 Definition and classification of chronic pain ...... 1 The biopsychosocial model and interdiciplinary treatment ...... 2 Chronic pain management in practice ...... 5 Chronic pain education ...... 6 Attitudes and beliefs in health care ...... 7 Novel pharmacological chronic pain treatment ...... 8 Palmitoylethanolamide general background ...... 9 PEA mechanisms of action ...... 11 Comparison PEA and fibrates ...... 13 PEA levels in pain and inflammation ...... 14 PEA in clinical trials ...... 15 PEA as a novel treatment for oral lichen planus ...... 17 Aims of the thesis ...... 20 Methods and data analysis ...... 21 1. Surveys as a research strategy (Paper I and II) ...... 21 1.1 Questionnaire developement ...... 21 1.2 Participant selection ...... 22 1.3 Method for evaluating attitudes: the Health Care Providers' Pain and Impairment Relationship scale (HC-PAIRS) ...... 22 1.4 Eliciting treatment preferences: Best-worst scaling experiment (BWS) ...... 23 1.5 Open-ended questions and their analysis ...... 26 2. Cell culture (Paper III and IV) ...... 26 3. Quantitative Polymerase Chain Reaction (qPCR) (Paper III and IV) ...... 28 3.1 RNA extraction ...... 28 3.1.1 Cell lines ...... 28 3.1.2 Oral lichen planus biopsies ...... 28 3.2 Complementary DNA (cDNA) conversion ...... 29 3.2.1 Cell lines ...... 29 3.2.2 Oral lichen planus biopsies ...... 29 3.3 Reverse transcription Real-time qPCR (RT-qPCR) ...... 30 4. Cyclooxygenase activity assay (Paper III) ...... 32 5. Statistics ...... 33 5.1 Resampling techniques (Paper II and III) ...... 33 5.1.1 Permutation test ...... 33 5.1.2 Bootstrapped confidence intervals ...... 34 5.2 Conditional logit regression (Paper II) ...... 35

Results ...... 36 1. Attitudes and beliefs amongst final year medical students (Paper I) ...... 36 1.1 Survey demographics ...... 36 1.2 HC-PAIRS scores ...... 37 1.3 Students' thoughts about chronic pain education ...... 38 2.Factors affecting treatment choices made by Swedish GPs and final year medical students (Paper II) ...... 40 3. Does PEA have a direct effect upon COX-2? (Paper III) ...... 46 3.1 Effects of PEA in RAW 264.7 cells ...... 46 3.1.1 mRNA levels of Ptgs2, Faah and Naaa ...... 46 3.1.2 COX-2 protein levels ...... 48 3.1.3 Prostaglandin and other oxylipin levels ...... 48 3.1.4 Effects of PEA upon the catalytic activity of COX-2 ...... 48 4.The levels of PEA, its synthesising, and hydrolysing enzymes in OLP-patients relative to the levels of COX-2 and its prostaglandin products (Manuscript IV) ...... 50 4.1 mRNA levels of NAPE, PTGS2, FAAH and NAAA in biopsies from OLP patients ...... 51 4.2 mRNA levels of NAPE, PTGS2, FAAH and NAAA in oral carcinoma cell lines SCC-25 and CAL27 ...... 53 4.3 NAE and PG levels in biopsies from OLP patients ...... 54 Discussion ...... 56 Attitudes and beliefs about chronic pain patients and views on chronic pain education by final year medical students in Sweden and Australia ...... 56 Factors impacting the treatment choices of final year medical students and general practitioners ...... 58 Effects of PEA on COX-2 expression and activity in RAW 264.7 cells ...... 62 Direct effects of PEA on COX-2 enzymatic activity ...... 64 Increased levels of PTGS2 and possibly NAPEPLD in OLP patients ...... 64 Effects of TNF-a upon the expression of PTGS2 in oral carcinoma cells ...... 65 NAE and PG levels in biopsies from OLP patients ...... 66 Future perspectives ...... 68 Acknowledgements ...... 70 References ...... 73 Appendix ...... 97

Original papers

The present thesis is based on the following papers and manuscripts, in the text referred to by their Roman numerals. Permission to republish the original papers have been granted from the publisher.

I. Rankin L, Stålnacke B. M, Fowler C. J, Gallego G. (2018) Differences in Swedish and Australian medical student attitudes and beliefs about chronic pain, its management, and the way it is taught. Scand J Pain. 18(3): 533-544.

II. Rankin L, Fowler C. J, Stålnacke B. M, Gallego G. (2019) What influences chronic pain management? A best-worst scaling experiment with final year medical students and general practitioners. British J Pain. 13(4): 214-225.

III. Gabrielsson L, Gouveia-Figueira S, Häggström J, Alhouayek M, Fowler C. J. (2017). The anti-inflammatory compound palmitoylethanolamide inhibits prostaglandin and hydroxyeicosatetraenoic acid production by a macrophage cell line. Pharmacol res perspect. 5(2): e00300.

IV. Rankin L, Gouveia-Figueira S, Danielsson K, Fowler C.J. Manuscript. On the balance between prostaglandins and anti- inflammatory N-acylethanolamines in oral lichen planus

Appendix: Gabrielsson L, Mattsson S, Fowler C.J. (2016). Palmitoyl- ethanolamide for the treatment of pain: pharmacokinetics, safety and efficacy. Br J Clin Pharmacol. 82(4): 932–942.

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Abstract

Chronic pain will affect one in five adults during their lifetime, and it exerts a heavy burden on society with major physiological, psychological, social, and economic impacts. The current chronic pain curriculum taught to medical students in most settings is fragmented, inconsistent and inadequate and a vast majority of general practitioners considered their undergraduate training in chronic pain incomplete. Attitudes and beliefs amongst health care personnel are important and have shown to have impact on clinical management. There is currently a knowledge gap that needs to be addressed in this matter. In this thesis, through an online survey, the attitudes and beliefs of medical students in Sweden and Australia were surveyed. Additionally, we explored which factors influence chronic pain management amongst medical students in Sweden and Australia and Swedish general practitioners. We found that Swedish final year students have a more positive attitude towards chronic pain patients compared to Australian students. Both student cohorts perceived chronic pain management education in need of improvement. Furthermore, we found that the relative importance of factors that influence treatment decisions are formed early during undergraduate training, which further underlines the importance of improving pain curricula during undergraduate medical education in order to give the emerging workforce appropriate tools to manage chronic pain.

Management of chronic pain urgently requires novel, well-tolerated pharmacological treatment strategies. Palmitoylethanolamide (PEA) is a potential candidate for managing chronic pain. Its analgesic and anti- inflammatory effects have been observed in a range of experimental animal models and clinical trials. However, questions remain as to how PEA exerts its effects and how levels of PEA and its congeners are changed in states of pain and inflammatory disorders in humans. Treatment with PEA decreases cyclooxygenase 2 (COX-2) activity in animal models, but we found that PEA did not have direct effects upon the kinetic properties of COX-2 in a cell free system. However, COX-2 derived eicosanoid levels were reduced by PEA in lipopolysaccharide and interferon-g-stimulated RAW 264.7 cells. With respect to changes in PEA levels in a chronic inflammatory disorder, we investigated PEA levels, in addition to its synthesizing and hydrolysing enzymes in biopsies from patients with oral lichen planus (OLP). We found that the ratio of prostaglandins to PEA was increased in the OLP biopsy samples. Furthermore, PTGS2 mRNA levels (coding for COX-2) were increased in OLP-patients compared to controls relative to NAPEPLD mRNA levels (coding for a key enzyme in the synthesis of PEA). These results suggest that there is a relative deficit of PEA in OLP, raising the possibility that PEA might be useful for the treatment of this disorder.

Abbreviations

2-AG 2-arachidonoylglycerol

11-HETE 11-hydroxy-5Z,8Z,12E,14Z-eicosatetraenoic acid

15-HETE 15-hydroxy-5Z,8Z,11Z,13E-eicosatetraenoic acid

AEA N-arachidonoylethanolamine, anandamide

BIBD Balanced incomplete block design

BSA Bovine serum albumin

BWS Best-worst scaling

CI Confidence interval

COX2 Cyclooxygenase 2

Ct Cycle threshold

DCE Discrete choice experiment

DMSO Dimethyl sulfoxide, solvent

EtOH Ethanol, solvent

FAAH Fatty acid amine hydrolase, catabolic enzyme

GP General practitioner

FBS Fetal bovine serum albumin

HAS Human serum albumin

HC-PAIRS Health care providers’ pain and impairment relationship scale

HCP Health care professional

IASP The International Association for the Study of Pain

ICD-11 International Classification of Diseases 11th Revision

IFN-g Interferon gamma

IL-1ß Interleukin 1 beta

IL-8 Interleukin-8

LPS Lipopolysaccaride

MMR Multimodal rehabilitation

NAAA N-acylethanolamine-hydrolysing acid amidase, catabolic enzyme

NAPE-PLD N-acyl phosphatidylethanolamine specific phospholipase D, synthesising enzyme

NSAID Nonsteroidal anti-inflammatory drug

N Number of separate experiments

NRS Numeric Rating Scale

PEA Palmitoylethanolamide

PGD2 9α, 15(S)-dihydroxy-11-oxo-prosta-5Z,13E-dien-1-oic acid, prostaglandin D2

PGE2 9-oxo-11α,15(S)-dihydroxy-prosta-5Z,13E-dien-1-oic acid, prostaglandin E2

SBU Swedish Agency for Health Technology Assessment and Assessment of Social Services

SD Standard deviation

TNF-a Tumour necrosis factor alpha

VAS Visual analogue scale

Enkel sammanfattning på svenska

Långvarig smärta är ett tillstånd som drabbar många personer världen över någon gång under deras livstid. Tillståndet har betydande social, fysisk, psykisk och ekonomisk inverkan på både samhälle och individ. Nuvarande behandling ses ofta som otillräcklig både bland patienter och läkare. Bland de hinder som beskrivits föreligga för att uppnå optimal behandling av långvarig smärta förekommer bland annat avsaknad av säkra och effektiva läkemedel samt en otillräcklig grundutbildning av hälso- och sjukvårdspersonal i långvarig smärta.

Behandling av långvarig smärta kräver en patientcentrerad helhetssyn som innefattar patientens biologiska, sociala och psykiska hälsa. Denna helhetssyn kräver samarbete mellan flera professioner inom hälso- och sjukvård. Ytterligare en viktig aspekt för behandling av långvarig smärta är patientens och läkarens inställning och attityd till sjukdomen, där till exempel ett stigmatiserande synsätt kan överföras till patienten och försämra behandlingen. Gällande utbildning i långvarig smärta har undersökningar gett vid hand att en klar majoritet av läkare i Europa uttryckt att deras utbildning i ämnet på grundnivå är otillräcklig. Undersökningar av nuvarande utbildning i långvarig smärta vid europeiska universitet visar att bara 12 timmar i genomsnitt ägnas åt ämnet, och att undervisningen oftast sker inom andra kurser istället för i en separat kurs.

I denna avhandling undersöktes attityder och inställning till långvarig smärta bland svenska och australienska läkarstudenter, samt uppfattningar om den utbildning de fått i ämnet. Vi fann att svenska läkarstudenter hade något bättre inställning till patienter med långvarig smärta än australienska läkarstudenter, men att båda studentgrupperna tycker att det behövs en förbättring både med avseende på utbildning och behandling av långvarig smärta. Vidare undersökte vi vilka faktorer svenska- och australienska läkarstudenter samt svenska allmänläkare prioriterar när de väljer behandling till en fiktiv patient som nydiagnostiserats med långvarig smärta.

Vi fann att svenska studenter och allmänläkare generellt prioriterade samma faktorer, medan australienska studenter prioriterade något annorlunda. Den största skillnaden mellan de svenska kohorterna och den australienska var att australienska studenter prioriterade sin egen professionella erfarenhet väldigt lågt, även i jämförelse med de svenska studenterna. Bland de faktorer som prioriterades högst bland alla tre kohorter fanns patientens beskrivning av smärtan, smärthistorik och tidigare behandling, och bland de som prioriterades lägst fanns bland annat social support och patientens preferenser, båda viktiga komponenter i den helhetssyn som bör has vid behandling av långvarig smärta. En faktor som generellt ignorerades av alla tre kohorter var patientens följsamhet

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till behandling. Bristande följsamhet har visats vara väldigt vanligt vid behandling av smärta och har naturligtvis en negativ inverkan på behandlingsutfallet. Dessa fynd betonar vikten av att förbättra utbildningen i långvarig smärta redan på grundnivå för att kunna förse nya läkare med rätt verktyg för att behandla sina patienter.

Bristen på effektiva läkemedel med en mild biverkningsprofil är det andra hindret för optimal behandling av långvarig smärta som studerats i denna avhandling. Nuvarande läkemedel som används mot långvarig smärta är framförallt svaga opioider, icke-steroida antiinflammatoriska läkemedel (NSAIDs), paracetamol och starka opioider. Förutom att några av dessa ofta ger betydande biverkningar i form av förstoppning med opioider, risk för gastrointestinala skador eller hjärt- kärl påverkan med NSAIDs och risk för förgiftning med paracetamol, så ses ofta bara en liten lindring av smärtan i kliniska studier jämfört med placebo för till exempel NSAID-läkemedel.

En möjlig ny kandidat för behandling av långvarig smärta är fettsyraamiden palmitoyletanolamid (PEA). PEA är en kroppsegen substans som vid behandling uppvisat anti-inflammatoriska egenskaper i en rad cellmodeller, inklusive smärtstillande egenskaper i djurmodeller och kliniska prövningar på människa. De kroppsegna nivåerna av PEA och de enzymer som är involverade i dess nedbrytning och syntes tycks kunna vara förändrade i en rad smärt- och inflammatoriska tillstånd. Verkningsmekanismen för PEA har undersökts sedan 1990-talet och verkar huvudsakligen ske genom effekter på peroxisome proliferator activated receptor alpha (PPAR-a) men det finns möjlighet att verkan sker genom ytterligare mekanismer. En möjlig mekanism som undersöktes i denna avhandling är en direkt påverkan på cyklooxygenas 2 (COX-2) då det har observerats med andra substanser. Vi fann att PEA minskade nivåerna av prostaglandiner, de inflammationsframkallande substanser som COX-2 bildar, men detta utan att påverka genuttrycket av COX-2 i en musmakrofag-cellinje stimulerad med lipopolysackarider från bakterier, eller att i ett cellfritt system uppvisa någon direkt effekt på COX-2’s kinetiska egenskaper.

Slutligen undersökte vi nivåer av PEA och relaterade enzymer, COX-2, och prostaglandiner i biopsier från patienter med den inflammatoriska sjukdomen oral lichen planus (OLP) i syfte att utröna om PEA kan vara ett möjlig behandling för dessa patienter som för närvarande är i behov av nya behandlingsalternativ. I biopsier från OLP-patienter fann vi att balansen mellan prostaglandiner och PEA är störd i patienter med OLP. Detta fynd öppnar möjligheter för att undersöka om PEA har effekt på OLP i kliniska prövningar. Dessutom såg vi i enlighet med andra studier, en uppreglering av COX-2 på gennivå i OLP-patienter, och denna ökning kunde vi efterlikna i kommersiella cellinjer från oral slemhinna med hjälp

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av TNF-a, som är en viktig pro-inflammatorisk substans i utvecklingen av många sjukdomar däribland OLP. Vidare studier krävs för att undersöka möjligheten att utveckla en cell-modell för OLP där PEA och andra potentiella läkemedel kan studeras, men ultimat vore om PEA som potentiell behandling av OLP kunde studeras i kliniska prövningar.

Introduction

Chronic (non-malignant) pain is a major public health problem. Its prevalence in Europe is 19 %, with similar results in other parts of the world. Thus one in five adults will experience chronic pain in their lifetime (Breivik et al. 2006, Harker et al. 2012, Blyth et al. 2001). In regards to chronic pain types, musculoskeletal pain is the most common cause (Breivik et al. 2006), represented by conditions such as chronic back pain and osteoarthritic pain.

Chronic pain exerts a heavy burden on society with major physiological, psychological, social, and economical impact (Fornasari et al. 2012, Sarzi- Puttini et al. 2012, Breivik et al. 2006). The prevalence for co-occurring depression or anxiety is high, 25-40 % (Gerdle et al. 2020). The economic cost of chronic pain in Europe has been estimated at €300 billion, which includes both direct and indirect costs resulting from loss of income and welfare (EFIC 2010). In Australia, it has been estimated as the third most costly health problem in the country (MBF Foundation 2007). The cost of chronic pain in the US exceeds the individual total cost of heart disease, cancer and diabetes (Gaskin and Richard 2012). Furthermore, current treatment of chronic pain is often inadequate (Breivik et al. 2006), and novel, well-tolerated treatment strategies are needed.

Definition and classification of chronic pain Pain in general is a complex phenomenon that is described by The International Association for the Study of Pain (IASP) as “An aversive sensory and emotional experience typically caused by, or resembling that caused by, actual or potential tissue injury” (new definition 2020) (IASP Task Force on Taxonomy 1994).

Chronic, or prolonged pain can be defined as pain that continues for more than three months and thus “has persisted beyond normal tissue healing time”. Previously, chronic pain was considered to be comparable to acute pain, only differing in duration. It is however now clear that it is a disease in its own right with structural changes that can differ and develop from those of the initial cause (Treede et al. 2019). Based on mechanism, pain is divided into neuropathic pain, nociceptive pain, and nociplastic pain (Gerdle et al. 2020). Neuropathic pain is estimated to affect between 3-8 % of the population (Bouhassira et al. 2008) but numbers up to 10 % have been found by some researchers (van Hecke et al. 2014). It is defined as “Pain caused by a lesion or disease of the somatosensory nervous system” by the IASP (Jensen et al. 2011). It can be central or in the periphery and can result from many causes such as trauma; toxin exposure; metabolic

disturbances such as during diabetic neuropathy; or the cause of a neurodegenerative, autoimmune or vascular conditions such as multiple sclerosis or stroke (Scholz et al. 2019). Nociceptive pain is the direct cause of nociceptor- activation from actual or threatening damage on tissue that is not neuronal (IASP Task Force on Taxonomy 1994). Chronic nociceptive pain is seen in arthritis or lumbago without neuropathic cause. Nociceptive pain is part of a normal functioning nervous system. Nociplastic pain is defined by the IASP as: “Pain that arises from altered nociception despite no clear evidence of actual or threatened tissue damage causing the activation of peripheral nociceptors or evidence for disease or lesion of the somatosensory system causing the pain” (IASP Task Force on Taxonomy 1994). The current theory is that sensitisation occurs through activation of nociceptors and/or that the pain is spreading and worsening over time. Examples include widespread pain/fibromyalgia and pain associated with inflammatory bowel syndrome, also types of non-characterized lumbago. Patients can have one or several of these types of pain concurrently (Gerdle et al. 2020).

In 2019, the World Health Organization (WHO) International Classification of Diseases (ICD-11) was updated and chronic pain was introduced for the first time as its own code; a disease rather than a symptom. In ICD-11, chronic pain is divided into primary and secondary pain dependent upon whether or not the pain is a symptom of another primary cause (Scholz et al. 2011). Figure 1 shows a simplified classification of major pain conditions according to ICD-11.

The biopsychosocial model and interdiciplinary treatment The biomedical approach has traditionally been the benchmark for managing chronic pain where pharmacological treatment and surgery can be considered as the main interventions (Gatchel et al. 2014). However, during the last four decades, this approach has been superseded by a model whereby the co-occurring psychological and social symptoms accompanied with chronic pain should also be considered (Engel 1977, Gatchel et al. 2014).

According to the biopsychosocial model of chronic pain, pain is considered a subjective experience where physical pathology interacts with psychological and socioeconomic factors to modulate a patient’s report of symptoms and disability (Gatchel et al. 2007). The psychosocial and socioeconomic factors include cognition, attention, emotional disposition, functional and subjective disability, healthcare, family and work (Gatchel et al. 2014). A compilation of factors can be seen in Figure 2.

The biopsychosocial model for managing pain calls for a multidisciplinary approach utilizing several different professions such as physicians, physiotherapists, psychologists, nurses and occupational therapists, coordinated

Chronic widespread pain

Top level Complex regional diagnosis pain syndrome

Chronic primary Chronic Primary headache or Pain orofacial pain

Chronic cancer- Chronic primary related pain visceral pain

Chronic Chronic primary postsurgical or musculoskeletal posttraumatic pain pain

Chronic Chronic Pain neuropathic pain Chronic secondary Chronic secondary pain syndromes headache or orofacial pain

Chronic secondary visceral pain

Chronic secondary musculoskeletal pain

Figure 1. A simplified classification of major pain conditions according to ICD-11 (adapted from Treede et al. 2019). Top level diagnoses (in navy) include both primary and secondary chronic pain syndromes. First level diagnoses originating from chronic primary pain in pale navy. Chronic secondary pain first level diagnoses are not included in this figure, neither are 2nd or 3rd level diagnoses.

as one unit collaborating with the patient (Gatchel et al. 2014). The biopsychosocial model is now a widely-accepted model for understanding and managing chronic pain (Gatchel et al. 2007). Although the terms and definitions with respect to different types of multicomponent treatment approaches for chronic pain remain a subject of discussion, and a consensus is needed in order to evaluate the effectiveness of multicomponent treatment (SBU 2010, Kaiser et al. 2017).

In 2017, The IASP released definitions to distinguish between different types of management for chronic pain. In their definition, the term for an interdisciplinary treatment is “…multimodal treatment provided by a multidisciplinary team collaborating in assessment and treatment using a shared biopsychosocial model and goals” (IASP Task Force on Taxonomy 1994). In the literature, synonyms multidisciplinary, interdisciplinary and multimodal rehabilitation programmes can be found. Despite inconsistent terminology, the benefits of interdisciplinary teams in chronic pain management have been widely recognized in health research (Scascighini et al. 2008, Weiner & Nordin 2010, Sharma et al. 2019, Stein and Miclescu 2013, SBU 2010).

Drug effects

Age Genetic factors Biological

Nociception Comorbidity

Severity of disease

Social support Coping

Economical factors Stress

Work situation Cultural Catastrophising Social beliefs Psychological Expectations Social environment Cognition Activities of Mood/affect daily living

Figure 2. Biopsychosocial model of chronic pain adapted from Gatchel et al. 2007 and Fillingim 2017.

The current Swedish recommendations for chronic pain management are initially to adopt unimodal (e.g. pharmacological treatment or physiotherapy) rehabilitation performed in primary care. If sufficient effects are not seen through this strategy, multimodal rehabilitation (MMR) could be employed in primary care for patients with a less complex chronic pain condition in regards to psychological factors, or in specialist care, for patients with more complex chronic

pain and severe requirements for rehabilitation (Nationella Medicinska Indikatorer 2011). MMR is an evidence based rehabilitation method that is in line with the biopsychosocial model (Kamper et al. 2015) and is the state-of-the-art in chronic pain management (Scascighini et al. 2008). The most recent published report from the Swedish Agency for Health Technology Assessment and Assessment of Social Services (SBU) declared that in respect to pain relief, MMR programmes did not show benefits over conventional treatment regarding neck, shoulder and lower back pain. However, moderate/strong evidence for the benefits of MMR was seen in regards to overall health outcome measures, return to work and reduced sick leave (SBU 2010). Since then, studies that show beneficial outcomes for MMR programs with respect to pain relief, in association with musculoskeletal pain, such as lower back pain and neck pain, have been published (van Middelkoop et al. 2011, Pietilä-Holmner et al. 2020). Benefits of MMR in outcome measures such as return to work and sick-leave are also continuing to be confirmed (Fischer et al. 2019).

Chronic pain management in practice Most chronic pain patients are treated in primary care by general practitioners (GPs), indeed as many as 70 % of patients in Europe are managed in primary care and between 20-40 % of all primary care doctor appointments in Sweden and Finland are estimated to be due to pain conditions (Hasselström et al. 2002, Mäntyselkä et al. 2001, Breivik et al. 2006, Johnson et al. 2013). Only an estimated 2 % of chronic pain patients in Europe are managed by a pain specialist and as many as 43 % of patients investigated reported not receiving any treatment at all (Breivik et al. 2006). Overall, chronic pain management is considered to be rather unsatisfactory, both from the patient and health care professionals (HCPs) point of view. Physicians find it to be of the most difficult conditions to treat, and 40-79 % of patients in Europe have expressed dissatisfaction with their treatment (Johnson et al. 2013, Breivik et al. 2006, Bekkering et al. 2011). In an assessment of chronic nonmalignant pain management amongst medical residents, a majority reported that working with chronic pain patients was so unsatisfactory it impacted their wish to pursue a future career in primary care (Yanni et al. 2008).

Barriers to optimal chronic pain management have been identified in several studies: poor access to specialists; insufficient time at appointments; poor medication compliance; incongruence in expectations; poor communication; patients’ beliefs about managing their pain; not utilizing the full expertise of health care personnel; unsatisfactory chronic pain education and lack of safe and effective pharmacological treatment (Mills et al. 2016, National Drug and Alcohol Research centre 2012, Notcutt and Gibbs 2010, Stannard and Johnson 2003).

Clinical guidelines for chronic pain are often described as inconsistent, but evidence-based consensus documents regarding chronic pain care are beginning to appear. During the previous decade, several countries have developed national strategies for chronic pain management, where Australia is taking the lead in this regard (National Pain Summit Initiative 2010). The importance of non- pharmacological treatment is recognised, and many national guidelines promote multimodal approaches and state that pharmacological treatment should rather play an equal or a supporting role to non-pharmacological treatment in chronic pain management (Läkemedelsverket 2015, Healthcare Improvement Scotland 2013, British Pain Society 2013, NPS Medicinewise 2016). Implicit in these strategies is the opinion that stepping away from the biomedical view of chronic pain is essential for optimal treatment outcome. Moreover, patient centred-care, where the patient and/or their relative actively participates in planning and implementation of their care is now an integrated part of health care. As a consequence, considering patients’ preferences in healthcare has been shown in several studies to improve treatment outcomes (Say and Thomson 2003, Street et al. 2012, Ruland 1998, Kløjgaard et al. 2014).

Chronic pain education Chronic pain education has been identified as one of the single most important barriers to optimal chronic pain management (Notcutt and Gibbs 2010). The current chronic pain curriculum in most settings is fragmented, inconsistent and inadequate (Briggs et al. 2011, Watt-Watson et al. 2004, Webster et al. 2017). A pan-European survey by Johnson and colleagues concluded that 84 % of GPs considered their undergraduate training in chronic pain incomplete, and 89 % stated the need for more education. An analysis of undergraduate pain curricula across Europe in 2012-2013 by Briggs and colleagues showed that out of the 424 medical schools examined, eight out of 10 did not have any evidence of compulsory pain teaching at all. Pain was mostly taught within other areas and only 31% offered a dedicated pain module. Seven percent of the examined curricula did not have any evidence of pain education at all. A median of 12 hours was spent on pain education across Europe (Briggs et al. 2015). Seven schools in Sweden were evaluated and although they all offered pain education, only two were found to have a dedicated pain module and none of the education on pain was mandatory. There was no information on how many hours pain was taught in Swedish medical schools (Briggs et al. 2015). Similar results are reported from other parts of the world than Europe (Shipton et al. 2018).

In 1985, the IASP formed a subcommittee to address the substandard pain curriculums already. The current IASP curricula was formed in 2012 (updated in 2017) and consists of an inter-professional curriculum and an individual curriculum for different HCPs (IASP 2012). It addresses “The fundamental

concepts and complexity of pain… how pain is observed and assessed, collaborative approaches to treatment options, and application of pain competencies across the lifespan in the context of various settings, populations, and care-team models”. This curriculum can be considered to be the worldwide comprehensive outline for all medical curricula. However, this work has had a rather modest effect upon chronic pain curricula in medical schools (Loeser and Schatman, 2017), although some medical schools have integrated parts of the IASP curriculum into their curricula (Pöyhiä et al. 2005, Murinson et al. 2011, Tauben and Loeser 2013). Currently, Germany is the only country according to the IASP where the IASP curriculum has been integrated nationwide in their medical schools.

There is an evident knowledge gap in chronic pain education. Improving undergraduate pain curricula and overall pain education is key to meet increasing societal needs and challenges in this matter, with the aim of improving overall chronic patient care (Murinson et al. 2013, Loeser 2015).

Attitudes and beliefs in health care Beliefs can be described as the assumptions made about reality that will impact how events are interpreted (Gatchel et al. 2007). Attitudes and beliefs about pain are very important, on a societal level, since they will impact not only how patients respond to their disease and illness, but also how caregivers treat their pain patients (Hirsh et al. 2014). Furthermore, clinicians need to be aware of the impact of their own beliefs upon the beliefs of their patients. A systematic review of HPCs attitudes, and outcomes of patients with lower back pain showed that there is strong evidence for the association between their own personal beliefs and the beliefs of their patients (Darlow et al. 2012). General practitioners’ fear- avoidance beliefs have shown to influence the management of patients with lower back pain (Coudeyre et al. 2006). In addition, poor communication and little consideration of patient preferences are related to less beneficial treatment outcomes (Kløjgaard et al. 2014).

Patients with chronic pain often believe that their pain is unmanageable and they lack trust in their healthcare providers’ knowledge about chronic pain. Indeed, 28 % of respondents in a survey by Breivik and colleagues (Breivik et al. 2006) believed that their physician did not know how to control their pain. It is common that patients who are unaware of the biopsychosocial model of chronic pain feel as if they are not being heard and that their physician is focusing on treatment that is not in relation to their pain. This lack of understanding between doctors and patients is further highlighted in a study by Pedersen and colleagues reporting that doctors lack insight to their patients’ preferences and seem to overestimate their own importance to their patients (Pedersen et al. 2012). Thus,

there is a need for improved communication of chronic pain and the biopsychosocial model between HCPs and patients.

Novel pharmacological chronic pain treatment The above discussion has considered the biopsychosocial model with pharmacological treatment playing a supporting role. That is nonetheless an important role, and in this section, the current pharmacological approaches are summarised. Current treatment guidelines for pharmacological management of chronic pain, such as those provided by the Swedish Medical Products Agency or Healthcare Improvement Scotland, underline the importance of careful diagnosis, assessment and re-assessment, all based on the individual patient’s needs and on clinical evidence (Läkemedelsverket 2017, Healthcare Improvement Scotland 2013). For instance, the use of weak opioids in association to chronic pain is controversial. Yet, Breivik and colleagues found in 2006 that the most frequently prescribed drug classes for patients with moderate to severe chronic pain in Sweden are weak opioids such as tramadol (36 %) followed by NSAIDs (non-steroidal anti-inflammatory drugs, 27 %) paracetamol (26 %), cyclooxygenase-2 (COX-2 inhibitor (7 %)1 and strong opioids such as morphine (3 %). However, not all chronic pain is mediated through health care, since almost half of all chronic pain patients in Europe use non-prescription drugs (Breivik et al. 2006). These drugs, regardless of their source, have a range of unwanted effects. For example, constipation is a common and problematic side effect with opioids, gastrointestinal damage and increased risk for cardiovascular events with NSAIDs and risk of intoxication with paracetamol are issues to be considered (Labianca et al. 2012). A study from 1998 reported that in the United States (US) alone, there were an estimated 41000 hospitalisations and 3300 deaths each year among the elderly that were associated with NSAID use (Griffin 1998). The vast occurrence of side effects is an extensive problem in the drug treatment of chronic pain and not least for older people (Harker et al. 2012, Makris et al. 2014).

In addition to the issues discussed above, current pharmacological options for chronic pain are often insufficient. For instance, a Cochrane review from 2016 reported that NSAIDs had only a small benefit compared to placebo in regards to disability in chronic lower back pain. There was also a discussion of whether or not this change could even be clinically relevant, as the change was not large on a visual analogue scale (VAS 1-100mm) (Enthoven et al. 2016). Additionally, a recent systematic review from the American College of Physicians, investigating any new evidence for current treatment of lower back pain, found that NSAIDs only had a small to moderate effect, tramadol had a moderate effect, and

1 By definition, COX-2-selective inhibitors are NSAIDs, since they are non-steroidal and anti- inflammatory, but Breivik et al. (2006) gave them a separate grouping to the traditional NSAIDs such as ibuprofen and naproxen

paracetamol was found ineffective (compared to placebo) (Chou et al. 2017). Thus, there is an enormous need for novel chronic pain treatment that is effective and has reduced risk of side-effects.

Palmitoylethanolamide general background The endogenous N-acylethanolamine (NAE) palmitoylethanolamide (PEA, N-(2- hydroxyethyl)hexadecanamide, for structure see Figure 3) was first identified in soybean, peanut oil and egg yolk by Kuehl et al. (1957), who were following up on earlier studies by Coburn and colleagues suggesting that a component of egg yolk had beneficial effects in rheumatic arthritis (Coburn and Moore, 1943; Coburn et al. 1954). It was identified in mammalian tissues 10 years later (Bachur et al. 1965). PEA is one of the most abundant of the NAE’s in mice and rat tissues where it has been observed in range of between 5-50 000 pmol/g tissue (Hansen 2013). In humans, estimated amounts in various fluids such as urine, plasma, and breast milk range from 0.4-126 pmol/g (Hansen 2013).

PEA was used clinically in the 1970s in former Czechoslovakia, under the name Impulsin, following successful clinical trials in treating acute respiratory diseases in school children and soldiers in the 1970s (Kahlich et al. 1979, Mašek et al. 1974). PEA was however withdrawn from the market, although it has reappeared in some parts of Europe as a nutraceutical (NormastTM, PelvilenTM [Epitech]), PeaPureTM [JP Russel Science Ltd]) and a food supplement, as well as being a constituent of a cream (Physiogel AITM, Stiefel) marketed for dry skin. It is also marketed for veterinary uses (skin conditions, RedonylTM, [Innovet]).

Figure 3. Structure of PEA

PEA and other NAEs (such as the endogenous cannabinoid receptor ligand N- arachidonoylethanolamide [anandamide, AEA] and the satiety agent N- oleoylethanolamide [OEA]) are synthesized on demand directly from membrane glycerophospholipids. Upon synthesis, PEA can re-locate within the lipid bilayer but only leaves it upon encountering an appropriate binding carrier protein that transports it to its target (Hansen 2013). PEA is synthesised through different pathways (see Figure 4 for a schematic overview of NAE synthesis). The N- acylation-phosphodiesterase pathway, sometimes referred to as “the classic pathway”, was determined 30 years ago as the main biosynthetic pathway of NAEs. In this pathway, N-acyl phosphatidylethanolamine phospholipase D (NAPE-PLD) catalyses the formation of NAE’s from N- acylphosphatidylethanolamines (NAPE’s) in two enzymatic reactions (Schmid et al. 1990). Other pathways of NAE synthesis include: NAPE-PLD-independent formation of NAEs from NAPE through other enzymes such as lysophospholipase

D-type enzymes, secretory phospholipase A2 (PLA2) and a/b-hydrolase domain- containing protein 4 (ABHD4) belonging to the alpha/beta hydrolase superfamily. NAEs can also be formed from N-acylated plasmalogen-type ethanolamine phospholipid (N-acyl-plasmenylethanolamine) through both NAPE-PLD- dependent and -independent pathways (Rahman et al. 2014). However, the N- acylation-phosphodiesterase pathway seems to be the rate-limiting step in NAE- formation (Ueda et al. 2001).

PEA and other NAEs are hydrolysed through two enzymes into free fatty acids and ethanolamine, a process that enables rapid clearing and regulation of NAE levels (Hussain et al. 2017) (See Figure 4). Fatty acid amide hydrolase (FAAH) was the first to be identified (Schmid et al. 1985), and subsequently shown to have a wide substrate specificity, including AEA and other NAEs as well as N- acylamides and N-acyl taurines (Deutsch and Chin 1993; Boger et al. 2000). The second enzyme to convert NAEs was first identified later in 1999, as being N- acylethanolamine acid amidase (NAAA) (Ueda et al. 1999, Ueda et al. 2001, Tsuboi et al. 2005). NAAA is a lysosomal enzyme found mainly in macrophages, it is effective in acidic environment (pH 5, compared to 8,5-10 for FAAH) and prefers PEA over other NAE’s (Ueda et al. 2001, Tsuboi et al. 2005).

N-acylphosphatidylethanolamine (NAPE)

NAPE-PLD

N-acylethanolamine (NAE)

Phosphatidic acid FAAH

NAAA

Ethanolamine + Free fatty acid

Figure 4. NAE synthesis (through the classic N-acylation-phosphodiesterase pathway) where N-acylphosphatidylethanolamine (NAPE) is hydrolysed by NAPE- hydrolysing PLD (NAPE-PLD) and degradation through hydrolysing enzymes FAAH and NAAA forming ethanolamine and the corresponding free fatty acid. Adapted from Rahman et al. 2014).

PEA mechanisms of action The anti-inflammatory and analgesic mechanisms of PEA have been under investigation since the 1990’s (Jaggar et al. 1998, Calignano et al. 1998, Calignano et al. 2001, Farquhar-Smith et al. 2002). The effects of PEA upon referred hyperalgesia in an inflamed bladder model were prevented by administration of the CB2 receptor antagonist/inverse agonist SR144528 (N-[(1S)-endo-1,3,3- trimethyl bicyclo [2.2.1] heptan-2-yl]-5-(4-chloro-3-methylphenyl)-1-(4- methylbenzyl)-pyrazole-3-carboxamide) but not by the CB1 receptor antagonist/inverse agonist SR141716A (rimonabant, N-(piperidin-1-yl)-5-(4- chlorophenyl)-1-(2,4-dichlorophenyl)-4-methyl-1H-pyrazole-3-carboxamide) (Farquhar-Smith et al. 2002). PEA, however, has a low affinity towards CB2

receptors (Lambert et al. 1999). The currently favoured mechanism of action of PEA is that it activates peroxisome proliferator activated receptor alpha (PPAR- α). In 1996, Devchand and colleagues found that PPAR-α-deficient mice had a prolonged inflammatory response (Devchand et al. 1996). Furthermore, in a mouse model of peripheral neuropathy, pain relief and neuroprotection observed with PEA treatment was diminished in PPAR-α-null mice (Di Cesare Mannelli et al. 2013). In 2005, Lo Verme and colleagues reported that PEA activates PPAR-α with a half-maximal effective concentration (EC50) at concentrations around 3 µM (Lo Verme et al. 2005). PPAR-α is expressed in numerous mammalian tissues such as liver, heart, intestine and immune cells including macrophages (Pontis et al. 2016).

The anti-inflammatory effects seen with various PPAR-α-agonists has been proposed to be due to inhibition of nuclear factor-kB (NF-kB)-activity and thereby inhibition of release of proinflammatory cytokines such as TNF-α, IL-1ß and IL-6 downstream of NF-kB (Bougarne et al. 2018). A similar pattern of inhibition is seen with PEA (Lo Verme et al. 2005).

Inflammation is a complex process orchestrated by an array of immune cells and the release of pro- and anti-inflammatory mediators. Along with the anti- inflammatory effects of PEA mediated through PPAR-α, there are potential additional mechanisms by which PEA is involved in resolving inflammation. Some of the first mechanistic studies on the anti-inflammatory effects of PEA revealed the ability of PEA to down-modulate mast cell activity by acting as a negative feedback loop on activated mast cells. The mechanism was named “autacoid local inflammation antagonism” or ALIA and PEA was entitled to be an aliamide; a locally released molecule that is released in response to injury or inflammation to counteract it (Aloe et al. 1993, Levi-Montalcini et al. 1996). Cerrato and colleagues showed that PEA could down-modulate histamine, prostaglandin D2 (PGD2) and TNF-α-release from anti-IgE-activated canine mast cells (Cerrato et al. 2010). In 2011, De Filippis and colleagues first found evidence for the connection between mast cell stabilisation and pain in a rat model of chronic inflammation (De Filippis et al. 2011). In vitro, however, conflicting results upon the ability of PEA to affect mast cell function have been seen as PEA failed to modulate degranulation from a rat basophilic leukaemia cell line (Granberg et al. 2001). Additionally, histamine release was not affected by PEA in anti-IgE activated rat peritoneal mast cells (Lau and Chow 2003), and had no effect on mast cell degranulation in an ex vivo model using mouse skin slices (Jonsson et al. 2006).

Evidence for the involvement of PEA in the inflammatory response mediated by macrophages have been found. Mouse peritoneal macrophages genetically

lacking the PEA synthesising enzyme NAPE-PLD were unable to down-regulate PEA production as part of normal inflammatory response to carrageenan, a commonly used pro-inflammatory agent, which confirms that PEA can be produced through other pathways. In the same study, stimulating RAW 264.7 mouse macrophages with bacterial endotoxin lipopolysaccaride (LPS) supressed NAPE-PLD, but not NAAA or FAAH, suggesting that the suppression of NAPE- PLD and thus a potential lowering of PEA levels is part of the normal inflammatory response (Zhu et al. 2011). In a pre-clinical mouse model of neuropathic pain, it was seen that PEA prevented macrophage infiltration to damaged nerves, hence relieving them from some neuropathic damage (Di Cesare Mannelli et al. 2013). Pre-incubating macrophages with PEA also increased phagocytosis and increased the survival of mice infected by E. coli K1 (Redlich et al. 2014). Furthermore, Li and colleagues found that NAAA inhibitor F215 increased PEA levels and produced anti-inflammatory effects such as inflammation resolution and clearance of neutrophils in LPS-stimulated, but not unstimulated mouse alveolar macrophages (Li et al. 2018). Recently, Rinne and colleagues evaluated the effects of PEA on a mouse model of atherosclerosis followed by comparisons with human atherosclerotic plaques. They found, in line with previous studies, that NAPE-PLD expression, and thus PEA-levels were reduced in activated primary mouse macrophages, a finding that was also seen in human atherosclerotic plaques. Also, mice treated with PEA had a reduction of plaque formation and PEA increased phagocytosis through the orphan receptor GPR55 (Rinne et al. 2018).

Comparison of PEA and fibrates As discussed above, the currently-favoured mechanism of action of PEA is as a PPAR-α agonist. Hence, it would be expected that the pattern of clinical effects of PEA and the clinically well-established fibrate group of PPAR-α agonists (examples include gemfibrozil, fenofibrate and bezafibrate), used for the treatment of hyperlipidaemia and hypercholesterolaemia, should match each other. Certainly, anti-inflammatory effects of fibrates are seen in experimental models (Ann et al. 2015, Wahba et al. 2016, Usui-Ouchi et al. 2017, Ju et al. 2017). PEA and fenofibrate, however, differ in their unwanted effects profile: at the level of common unwanted effects (i.e. > 1:100, which is possible to glean from the clinical studies so far undertaken with PEA, see Gabrielsson et al. 2016). PEA appears to be very well tolerated, whereas fenofibrate has a range of unwanted gastrointestinal effects including abdominal pains (FASSa). Whilst PEA applied topically reduces the need for glucocorticoid treatment in patients with atopic eczema (Eberlein et al. 2008), eczema is a common unwanted effect of gemfibrozil treatment (FASSb). Although of course this may be an off-target effect of gemfibrozil since it is less common with fenofibrate and bezafibrate (FASSa, FASSc), these observations raise the possibility that the anti-inflammatory effects

of PEA can be mediated by additional targets. Several targets have been suggested, such as the existence of a CB2-like receptor, the orphan receptors GPCR 55 (GPR55, see above) and GPR119, and “entourage” effects of PEA upon AEA and its action on the transient receptor potential cation channel subfamily V member 1- (TRPV1), peroxisome proliferator activated receptor gamma (PPAR- γ) and CB receptors (De Petrocellis et al. 2002, Smart et al. 2002, Overton et al. 2006, Ryberg et al. 2007, Costa et al. 2008, Ho et al. 2008, Lan et al. 2009). One possibility, however, that has not been investigated, is that PEA can directly interact with the prostaglandin synthesising enzyme cyclooxygenase 2 (COX-2) and change its catalytic properties and through this mechanism inhibit production of pro-inflammatory prostaglandins such as PGD2 (Ricciotti and FitzGerald 2011). Such effects have been seen with long-chain fatty acids (Meijerink et al. 2015), and with fenofibrate (Prasad et al. 2018), and could be the mechanism behind decreased COX-2 activity seen in vivo in a rat model of inflammation following PEA treatment (Costa et al. 2002).

PEA levels in pain and inflammation Given the anti-inflammatory effects of PEA, levels of PEA might be expected to change in inflammation and pain, either in a manner suggestive of a loss of protection against the inflammation (i.e. decreased levels) or of a compensatory synthesis to limit the inflammation (i.e. increased levels). In several animal models, pro-inflammatory agents such as carrageenan and LPS have resulted in a decrease of PEA-levels. In mice with chronic intestinal inflammation, Capasso and colleagues found decreased levels of PEA in the small intestine (Capasso et al. 2001). In another mouse model of inflammation, PEA-levels were decreased in infiltrating leukocytes following inflammatory induction by carrageenan (Solorzano et al. 2009). In addition, PEA levels were found to be decreased in a rat model of inflammation, induced by carrageenan and measured by granuloma formation (De Filippis et al. 2010). There are also other inflammatory and pain conditions in which PEA have been observed to be increased. A mouse model of multiple sclerosis instead showed increased PEA levels (Baker et al. 2001), and elevated PEA levels were also found in a rat model of osteoarthritis (Sagar et al. 2010). Increased mRNA levels of NAPE-PLD, and decreased levels of NAAA and FAAH have also been shown in cows suffering from post-partum uterine inflammation (Bonsale et al. 2018).

Human studies in this respect are few and far between. PEA-levels have been estimated in a couple of pain conditions: synovial fluid from patients with rheumatoid arthritis and osteoarthritis contains lower amounts of PEA compared with control subjects (Richardson et al. 2008). Increased PEA levels have been found in patients with chronic neck/shoulder pain compared to controls (Ghafouri et al. 2011). In a later study, Ghafouri and colleagues consistently found

increased PEA levels in patients chronic neck/shoulder pain before and after pain-inducing activity. However, the same results were not seen in patients with chronic widespread pain, where significantly decreased levels were found after pain-inducing activity. However, the initial levels of NAEs were similar between the groups (Ghafouri et al. 2013). In a similar study, Stensson and colleagues did not find altered levels of PEA in correlation to pain in a case-control study of chronic widespread musculoskeletal pain (other NAEs were however significantly increased) (Stensson et al. 2016). Finally, PEA levels were found to be decreased in patients with post-operative pain from a total knee arthroplasty who received intrathecal morphine treatment compared to placebo (Kaczocha et al. 2018). Thus, PEA levels are differently altered in different pain conditions. PEA levels were found to be increased in endometrial stromal cells in women with endometriosis, and to be higher in women with moderate to severe dysmenorrhea estimated using a VAS-scale (Sanchez et al. 2016).

PEA-related changes such as altered levels of FAAH and NAAA have also been found in some inflammatory and/or pain conditions: in patients with ulcerative colitis, mRNA levels of PPAR-α and NAAA are decreased, whereas FAAH and iNOS are increased (Suárez et al. 2012). Moreover, mRNA levels of FAAH were found to be increased in patients with chronic lower back pain, but not in patients with acute low back pain (Ramesh et al. 2017). Clearly, more studies with respect to the levels of PEA and other NAEs and their synthetic and catabolic enzymes in human inflammatory and chronic pain conditions are needed.

PEA in clinical trials A general problem when evaluating pharmacological treatment for chronic pain is that trials are not conducted during long enough periods (usually <3 months, see Chou et al. 2017). There is also a lack of description of relevant outcomes other than pain scores, such as quality of life, sleep problems and healthcare visits (Chou et al. 2017). Moreover, the quality of trials, even randomized controlled ones, for chronic pain is overall quite low (Moore et al. 2013) and many must be excluded from systematic reviews such as the one by Enthoven et al. (2016) due to unclarities in the study protocol. Even the included studies are sometimes of high risk of bias, or of other shortcomings such as low compliance or lack of description in randomization procedure (Enthoven et al. 2016). The clinical studies with PEA are a case in point (see Gabrielsson et al. 2016 for a summary of trials up to that publication date). Clinical trials with PEA are usually ongoing for less than 3 months when used for pain. There is however one exception, in terms of treatment length, when PEA was used as an add-on treatment for multiple sclerosis (MS) for 1 year, where its anti-inflammatory (reduction in pro- inflammatory markers) and analgesic properties of PEA (at the injection site of the original treatment) were seen after 6 and 12 months. PEA additionally

improved the quality of life according to MSQoL-54 questionnaire (Orefice et al. 2016).

Oral tablets are the most common dosage form of PEA in doses ranging from 300 mg to 1200 mg daily, and the most common pain evaluation tool is VAS (Gabrielsson et al. 2016). A PubMed search performed for clinical trials of PEA involving pain resulted in just over 20 articles. The overall quality of clinical trials on PEA is poor and there is a high number of small, unrandomized and uncontrolled trials (Gabrielsson et al. 2016). Often PEA is used as an add-on treatment and open-label trials are common. Open-label trials are susceptible to bias regarding efficacy, as patients are aware of which treatment they get, and placebo effects have been noted in clinical settings even if no placebo is given (Finniss et al. 2010).

The efficacy of PEA to reduce pain have been estimated in two recent meta- analyses. In the first, by Paladini and colleagues, a maximum difference of 3.52 points change with PEA on a VAS/numeric rating scale (NRS) scale compared to placebo was seen (Paladini et al. 2016). The latter meta-analysis found the efficacy of PEA to reduce pain scores estimated to be around 2 on a 10-degree VAS-scale (Artukoglu et al. 2017). Improvements in pain scores ranging below 2 points, or 15mm, on a NRS/VAS-scale have questionable clinical relevance, and patients with more severe pain require a larger change in scores in order to be considered clinically relevant (Bird and Dickson 2001, Ostelo et al. 2008). In our review, we estimated effect size of two different treatment regimens from the largest PEA-trial available enrolling 636 patients, in a double blind, randomized, placebo-controlled trial for PEA in treatment of lumbosciatica. We estimated Cohen’s d value to 1.35 (95 % CI 1.14–1.56) and hence a large effect size for 2 × 300mg PEA daily (this was not seen with the lower dose) (Guida et al. 2010, Gabrielsson et al. 2016). However, not all outcomes are positive, in 2016, PEA failed to prove being better than placebo in a double-blind multi-centre study using PEA as an add-on treatment over a period of 12 weeks in 73 patients with neuropathic pain (Andresen et al. 2016).

The tolerability of PEA is consistently reported to be very good. Indeed, it is very common that not a single adverse drug reaction (ADR) is reported in any of the studies, which is surprising as adverse effects are expected even in the placebo- group of double blind placebo controlled trials (Gabrielsson et al. 2016). The study conducted by Andresen et al did find ADRs in the same ratio as placebo (Andresen et al. 2016). However, considering the short treatment times, and low number of patients enrolled, the number of patients needed to treat in order to discover ADRs, especially more rare ones, were not met (Lewis 1981). As an example, 300 patients are needed for the 95 % likelihood of encountering one single common (1/100), or 3000 patients encountering an uncommon (1/1000),

ADR (Lewis 1981). In the above mentioned meta-analyses by Artukoglu et al. (2017) 786 patients had been treated with PEA, which is thus not enough patients to make confident claims about PEA safety as a systemic drug treating pain.

Longer trial periods are necessary to estimate clinical outcomes on chronic conditions in general, and PEA for chronic pain is no exception. Furthermore, the improved quality of clinical trials, including size, is a must in order to learn weather PEA has a future in treating chronic inflammation and pain.

PEA as a novel treatment for oral lichen planus Lichen planus (LP) is a common chronic inflammatory disease affecting the skin, genitalia and mucosa. Oral lichen planus (OLP) is the oral subtype of LP that affects approximately 2 % of the population, and predominantly women (McCartan and Healy 2008). OLP is found to impact quality of life, and the longer the disease continues, the higher level of depression, stress and worse quality of life is seen in patients (Radwan-Oczko et al. 2018). LP is characterized by relapses and remissions, although OLP have a tendency to be chronic, and have been observed to lead to malignant transformations in some cases (Eisen 2002, Olson et al. 2016).

There are several subtypes of OLP depending on clinical judgements: bullous; papular; erosive/ulcerative; atrophic; plaque-like and reticular (Alrashdan et al. 2016). In Figure 5, the characteristic white striae can be seen as well as a fibrin- coated ulcer. The aethiology of OLP is unknown. Several factors have been found to be associated with OLP or OLP-like lesions including: hepatitis C virus (Azab et al. 2018); a reaction to common medications such as beta blockers; ACE inhibitors; diuretics; oral hypoglycemics; NSAIDs and other drugs (Alrashdan et al. 2016); hypothyroidism (Siponen et al. 2010); and allergic reactions to dental materials such as amalgam (Bratel et al. 1996). OLP has also been compared to graft-versus-host disease as they share clinical and histological patterns (Sugerman et al. 2002).

Pathophysiological findings in OLP-patients include abnormal expression of pro- inflammatory cytokines such as TNF-α, IL-8, and genetic polymorphism of IFN- γ (Carrozzo et al. 2004, Adami et al. 2014), as well as activation of NF-κß (Santoro et al. 2003), all suggesting it is an inflammatory disease. Both antigen-specific and non-specific mechanisms are observed with OLP. Antigen-specific keratinocyte killing is mediated through activated CD8+ T-cells, derived from antigen presentation by major histocompatibility complex-1 on keratinocytes. CD8+ t-cells can also be activated through CD4+ lymphocytes. Examples of non- specific mechanisms in OLP include matrix metalloproteinase activation leading

Figure 5. A fibrin-coated ulcer inside the right cheek. Photograph courtesy of Dr. K Danielsson.

to a disruption of the basal membrane, as well as increased presence of degranulating mast cells, which release TNF-α, leading to de activation of more T-cells and the continuous destruction of the basal membrane (Sugerman et al. 2002). In addition, there is an increased infiltration of macrophages in OLP, where they have a pro-inflammatory role, presenting antigens and secreting TNF- α and IL-8 to activate the innate immune cells (Merry et al. 2012). The expression of COX-2 is upregulated in OLP and is suggested to be part of the proposed autoimmune character of the disease (Danielsson et al. 2012).

Treatment of OLP is currently suboptimal. Topical corticosteroids are the frontline treatment (Davari et al. 2014). The most utilized treatment is medium to high potency topical corticosteroid formulations such as creams and ointments that are applied to the affected area after drying (Carbone et al. 2003). Oral mixtures of corticosteroids are also used when the lesions are widespread and difficult to reach. Systemic treatment can be used as the next step in case of an exacerbation or if topical application is not helping (Davari et al. 2014). If corticosteroid-treatment is ineffective, mostly topical formulations of; immunomodulators such as methotrexate (Lajevardi et al. 2016) or cyclosporin (Levell et al. 1992), and biologic agents such as TNF-a-inhibitors (Zhang et al. 2011) are tried (Olson et al. 2016).

As described above, PEA has been found to inhibit COX activity and in animal models (Costa et al. 2002) and to inhibit NF-κß in experimental animal models (D’Agostino et al. 2009, Impellizzeri et al. 2015). Additionally, topical formulations of PEA in treating eczema have been successful (Eberlein et al. 2008, Yuan et al. 2014). Considering the supposedly mild ADR-profile of PEA, treatment for a chronic inflammatory condition such as OLP might be warranted. Conversely, it is possible that there is a relative deficit of endogenous PEA in OLP tissue and that such a deficit could contribute to OLP pathogenesis.

Aims of the thesis

The discussion above has identified important gaps in our knowledge in several aspects of pain and inflammation, ranging from treatment preferences among physicians and medical students to the mechanism(s) of action and role of PEA in OLP pathogenesis. The overall aim of the present thesis is to provide data with respect to some of these gaps.

Patient preferences on chronic pain management have been studied the past four decades, although not excessively. There is less extensive research examining physicians and medical students’ preferences, which has been shown to be an important factor contributing to patient care and clinical management. This limited literature underlines the lack of knowledge about stakeholders’ attitudes and preferences for managing chronic pain. Since attitudes and beliefs might influence these preferences; more research is needed to explore this issue. Thus, the formal aims of the first part of the thesis are:

1. To investigate differences in Swedish and Australian medical student attitudes and beliefs about chronic pain, its management, and the way it is taught. (Paper I)

2. To determine by use of a best-worst scaling experiment with final year medical students and general practitioners which factors influence chronic pain management (Paper II)

Palmitoylethanolamide (PEA) is a promising novel treatment for chronic pain and other inflammatory and pain-related diseases. The aims of the second part of the thesis concern the interaction and balance between PEA and COX-2. Thus, the aims of the second part of the thesis are:

3. To investigate if PEA has direct effects upon COX-2 (Paper III)

4. To investigate the levels of PEA, its synthesising, and hydrolysing enzymes in OLP-patients relative to the levels of COX-2 and its prostaglandin products (Manuscript IV)

Methods and data analysis

The methods used in this thesis are described in detail in the original papers included. This section offers a summary of methods used in this thesis including basic principles together with a discussion of their advantages and limitations. Liquid chromatography/mass spectroscopy experiments in papers III and IV were performed by co-author Gouveia-Figueira, Umeå University/Region Västerbotten and collection of tissue samples from OLP-patients and controls in paper IV were collected by co-author Danielsson, Umeå University, and these methods are thus not covered in this section.

1. Surveys as a research strategy (Papers I and II) Conducting a survey is sometimes regarded as an easy approach to research. However, conducting a survey on that basis without due thought and detailed planning is likely to result in a survey of poor quality and low value. The advantages of surveys as a research strategy include acquiring empirical data in large amounts to a rather low cost. Disadvantages include the difficulties of obtaining a high response rate, and it is common that the data produced in retrospect lack details that could have been useful (Kelley et al. 2003). Online surveys are preferable over post questionnaires (Brtnikova et al. 2008). Post questionnaires, are now found to have the lowest response rate at approximately 20%, compared to interviews face to face or over the telephone (Kelley et al. 2003) and in general response rates are declining. There are however varying numbers in the literature on what is an acceptable response rate; a range varying from 40 % - 80 % for questionnaires is mentioned by some authors (Fincham 2008, Story and Tait, 2019). However, response rates of election polls are reported to be as low as 10 % and still be valid (van Lenthe 2017). The concern about nonresponse bias has grown as nonresponse rates have grown, and several factors, such as for example the survey topic, can impact the response rate (Groves et al. 2004). One attempt to control for non-responder bias is to appreciate if the demographic data corresponds to the expected characteristics of the chosen sample, although this may not always be feasible.

1.1 Questionnaire developement Our questionnaire was developed using key literature for our aims by two groups (Houben et al. 2004, Hirsh et al. 2014, Hollingshead et al. 2015) who all have explored preferences, attitudes and factors for treatment decisions amongst medical students. As wording, design and order of questions can affect the responses it is important to carefully plan and design a questionnaire (Boynton and Greenhalgh 2004, Kelley et al. 2003). We were conducting the same survey

in two different languages, thereby with potential linguistic complications, and so we first created the survey in English, then translated it into Swedish and back- translated it into English to make sure the versions were matching.

We included some open-ended questions in our survey to explore the thoughts on medical training with respect to chronic pain. Although open-ended questions can be time consuming and difficult to analyse compared to closed questions, they can provide more comprehensive data, if they are asked correctly. It is important that open-ended questions are not double-barrelled, contain double negatives or are leading (Kelley et al. 2003). Furthermore, we employed a strategy of utilizing the expertise of medical students, GPs and chronic pain specialists to ensure that the terms and phrasing were in line with our aims of the study. We used an online survey tool that was previously used by one of the co- authors, that include the main design elements important to a visually good questionnaire design. However, another important aspect is the length of the questionnaire, as a too long survey is a pitfall that can lead to large attrition rates (Thwaites Bee and Murdoch-Eaton 2016). Surveys longer than 10 minutes should be avoided (Story and Tait, 2019). Pilot testing is yet another very important part of questionnaire development (Thwaites Bee and Murdoch-Eaton 2016) and so we did this with the intended respondent groups such as GPs from other counties in Sweden, and recently graduated medical students in Australia and Sweden.

1.2 Participant selection There is no definite answer to the question of what sample size is required for a survey since it depends on how data will be analysed, on the aims and available resources (Kelley et al. 2003). An important part of any research is to have the correct sample representative of your research question, otherwise sampling error can occur. As we utilized purposive sampling on a subgroup level we used the demographic section in the survey to assure that we had obtained the appropriate participants. Sweden and Australia were selected as the two comparative countries for medical students’ attitudes and preferences. Sweden and Australia have similar levels of health welfare, infrastructure and management approaches, and both countries have issued national pain strategies in aim of improving overall chronic pain management. The final year medical students (or semester 9-11 for Swedish medical students) were chosen as they have acquired their scheduled chronic pain training. Finally, GPs were chosen as they are currently the main health care providers to chronic pain patients.

1.3 Method for evaluating attitudes: the Health Care Providers’ Pain and Impairment Relationship Scale (HC-PAIRS) When available, validated questionnaires for answering specific research questions should be used (Boynton and Greenhalgh 2004). We used HC-PAIRS to assess attitudes towards chronic pain. HC-PAIRS is widely used to measure health care professionals’ and students’ attitudes and beliefs about the relationship between pain and impairment (Houben et al. 2004). There is currently no gold standard method to evaluate medical students’ attitudes (Ung et al. 2016), and it should be mentioned that there are other tools available for assessing attitudes and beliefs. For instance, Knowledge and Attitudes Survey Regarding Pain’ (By Ferrell and McCaffery) is a widely-used instrument on students as it assesses knowledge as well as attitudes (Ung et al. 2016). The Pain Attitudes and Beliefs scale was originally developed and tested on physiotherapists and has not yet been as extensively used as HC-PAIRS (Ostelo et al. 2003).

HC-PAIRS is a questionnaire originally covering 15 items (statements) to which the respondents indicate the degree to which they concur on a 6-point Likert scale ranging from “totally disagree” to “totally agree” (Rainville et al. 1995). These items are then grouped into four factors: 1. “Functional expectations”, 2. “Social Expectations”, 3. “Need for cure” and 4. “Projected cognition”. The fourth factor was however not verified in their validation and is thus excluded by many authors (Rainville et al. 1995, Latimer et al. 2004, Overmeer et al. 2009, Slater et al. 2014, Jacobs et al. 2016, Epstein-Sher et al. 2017). A high score on the HC-PAIRS questionnaire indicates the belief that there is a strong relation between pain and impairment, resulting in an attitude that pain is related to disability and limitation of activities. As such, high scores reflect less beneficial attitudes towards pain. When utilizing a tool such as HC-PAIRS in another language, it is helpful if a version already has been used. We were kindly given a version published by Overmeer et al (2009), and made minor changes in language after pilot-testing such as the word “disabled” into “person with disability” in the Swedish version of the survey. Likert scale data have been accused of incorrect use and analysis as they are considered ordinal scales by some authors and thus should be analysed using non-parametrical statistics. However, they can be considered ratio scales and are amenable to parametric analysis (Carifio and Perla 2007, Brown 2011). We have chosen to present our data using both parametric and non-parametric analyses. HC-PAIRS have been further validated by Houben et al (2004) and Briggs et al (2013) showing conformity to clinical recommendations and good internal consistency.

1.4 Eliciting treatment preferences : Best-worst scaling experiment (BWS) Stated preference methods can be defined as methods that involve a trade-off from a set of two or more alternatives in a hypothetical situation. Stated preference methods can thus reveal what trade-offs health care personnel or patients are willing to make. In health care research, the result from this method reflect the value associated with a health programme or other attributes of a health service or product. There are a range of stated preference-based valuation methods available and these can be divided into cardinal methods (including VAS scales, standard gamble and time trade-off) and ordinal methods (including discrete choice experiments, ranking exercises and ordered categorical responses) (Ali and Ronaldson 2012).

Whilst in cardinal methods the order of preference is already established for the participant (e.g. 1-10 on a VAS-scale or number of minutes in a time-trade off), in ordinal methods, the attributes are presented with no relative importance to the participant. Further they are chosen so as to be easy to understand, have lower cognitive burden, and are free of some of the end point and context biases with cardinal methods and produce more consistent data that has good reliability (Ratcliffe et al. 2009)

Discrete choice experiments (DCEs) are perhaps the most used ordinal preference elicitation method in health care. DCE originates from applied economics and the random utility theory describing peoples’ choices as being their preferred choice in most cases (Thurstone 1927). DCEs have been increasingly used in health care during the past 20 years (Louviere and Lancsar 2009). In DCEs, respondents choose the best alternative amongst a finite set of

Table 1. A traditional example of a paired DCE.

Attributes Medicine A Medicine B

Dosing Once a day Three times a day

2-point decrease on a 4-point decrease on a Efficacy VAS scale VAS scale

Common adverse effects Nausea Gastrointestinal

Cost Low Moderate

Which medicine Medicine A Medicine B would you choose?

alternatives in a choice set. This method identifies the relative importance of different alternatives in this choice set, and thus it can be utilized to explore preferences for different treatments, the relative impact of different health care programme attributes, among other research questions (Louviere and Lancsar 2009, see table 1 for an example of the sort of research question amenable to DCE). The limitations of this method are that it requires more input than simple yes/no alternatives from respondents (due to the inefficiency of picking only one attribute per choice set) and it cannot make assumptions about the relative importance between different parameters. There is however another method derived from DCEs that can do exactly this, namely Best-worst scaling (BWS, Flynn et al. 2007).

In BWS, not only the best alternative, but also the worst is chosen, and hence provides more data through less extensive tasks than with traditional DCEs (Flynn et al. 2007). BWS was formed by Finn and Louviere (1992) and introduced to health care almost a decade ago (for review, see Flynn et al. 2007). The methods of BWS and DCE are found to give comparable and valid results in health care (Potoglou et al. 2011, Severin et al. 2013, Whitty and Gonçalves 2018).

In our survey, we utilized a BWS object case 1, that is designed to determine the relative importance of a list of attributes (Louviere et al. 2015). This particular BWS method was created with intention to replace rating scales and Likert scales as it eliminates scaling artefacts and social-desirability-bias and is increasingly used in health care over the past years (Cheung et al. 2016, Mühlbacher et al. 2016). The number of attributes determine the number of scenarios or “choice sets” required. We used a balanced incomplete block design (BIBD) which is the most used method to design choice sets in BWS Case 1. A BIBD is a table with b subsets of k items, where each item occurs r times and co-occur with each other λ times in two basic equations bk=vr and λ (v-1)=r(k-1) (Louviere et al. 2015). We selected a row-column design called Youden-type design from the library of BIBDs (Street and Street 1987) for our 11 attributes creating 11 choice sets where each object was repeated 5 times to be paired twice.

BWS is particularly beneficial for cross-cultural research over response scales as there is no need to worry about the scale being used differently between countries, and it has been found to be less prone to cultural bias as with Likert scales (Jürges 2007, Jaeger and MacFie 2010). It should be noted that there are critics of stated preference methods who argue that when faced with the same choice in real life, people might behave differently than they do in hypothetical situations. Case 1 has also been found to lack discriminating power as respondents might view attributes differently (Mülbacher et al. 2016). Furthermore, in some cases participants might find two items equally important, but the method does not permit ties. A suggestion to overcome these is to include a final question after

each choice set asking the importance of all, some or none of the attributes. In our survey we included general questions after the BWS task asking how they experienced the task in its whole and if there were any attributes they were missing in the task.

1.5 Open-ended questions and their analysis There are numerous methods for analysing qualitative data such as transcripts from interviews or focus-groups and observational studies. Amongst the most popular methods to interpret transcripts are grounded theory, qualitative content analysis and thematic analysis. All these methods involve some kind of coding where sentences are broken down into codes and then categorized into themes and further conceptualized into a theory. These methods are derived from different philosophical social genres in science and utilized differently. Grounded theory is a method used on data that is either observational or derived from interviews. It uses inductive reasoning (i.e. creates theory based on observations) and involves a more structured way of coding procedures than the other methods mentioned where the coding steps themselves result in a theory that is grounded in its natural context (Priest et al. 2002). Qualitative content analysis interprets manifest and latent content in data. It requires large data sets and is best used on whole interviews or observational protocols (Graneheim and Lundman 2004).

There is no gold standard for analysing open-ended questions. We chose to analyse our open-end questions with thematic analysis as described by Braun and Clarke (2006). In thematic analysis, the formation of themes is considered to be the central method and not a part within the method as a whole (as for instance with grounded theory). It is a flexible method as it is theoretically unbounded and thus does not require detailed theoretical knowledge. The method involves repeated reading of the material to identify, analyse and report patterns as codes within the data. It is important to give the full data set equal attention throughout this process. Identified codes are then refined and defined into themes that will be reviewed in relation to the codes. Finally, themes are defined and named and the report of findings produced (Braun and Clarke 2006). Limitations of thematic analysis lies mainly in its flexibility as the development of themes have been described to become inconsistent when it is not grounded in a theoretical framework as is the case for the other methods (Holloway and Todres 2003). Furthermore it does not allow to make claims about participants’ language use or their way of talking (Braun and Clarke 2006).

2. Cell culture (Papers III and IV) The cell lines used in the papers of this thesis were mouse leukemic macrophages RAW 264.7 (passage numbers 13 - 16), human squamous cell carcinoma CAL 27 (passage numbers 61 - 62) and human squamous cell carcinoma SCC-25 (passage numbers 30-31). The cells were all obtained from ECACC, Porton Down, UK. Cells were maintained at 37 °C, with 5 % CO2, in T75-cm2 flasks prior to seeding into wells depending on the assay. Cells were grown in their appropriate cell culture medium for each cell line supplemented with penicillin (100 U mL-1) and streptomycin (100 µg/ml). RAW 264.7 cells were cultured in Dulbecco’s Modified Eagle’s Medium with high glucose, CAL 27 cells were grown in ATCC DMEM 30- 2002 and SCC-25 were grown in DMEM/F12 HEPES supplemented with 400 ng/ml hydrocortisone. Media for all cell lines was supplemented with 10 % foetal bovine serum (FBS) prior to experiments, after which cells were serum starved. Cells were passaged or plated using a rubber policeman (RAW 264.7) or trypsin (CAL 27 and SCC-25).

RAW 264.7 cells are widely used in research as a macrophage model (Taciak et al. 2018). It is known that RAW 264.7 cells respond to inflammatory stimuli such as lipopolysaccharide (LPS) or interferon gamma (IFN-γ) by initiating an inflammatory response and the upregulation of COX-2 (Chen et al. 2003, Kang et al. 2006). Thus RAW 264.7 cells were stimulated with 0.1 µg/mL LPS and 100 U/mL IFN-γ (or phosphate-buffered saline (PBS)) for 30 min or 24 hours to induce an inflammatory response for the experiments investigating PEA mechanism(s) of action in paper III.

To our knowledge, cell lines have not extensively been used to study OLP. More often human keratinocytes from OLP-patients have been used as primary cultures (Sun et al. 2014). Although the use of such primary cultures is more sophisticated and more genetically accurate than the use of cell lines, it is time consuming and limiting in terms of expansion capacity and lifespan. SCC-25 and CAL 27 have been previously used for studying OLP but examples are few; Shi et al. (2015) used CAL 27 as part of their study on the involvement of a suppressive microRNA on the malignant progression of OLP, while Salem and colleagues used the SCC-25 cell line to study the involvement of histamine H4 receptor and human β-defensin 2 in OLP (Salem et al. 2015, Salem et al. 2019). SCC-25 and CAL 27 cell lines were grown in 24-well plates in 1 % FBS-supplemented culture media for 24 hours prior to treatment. Cells were treated either with vehicle (DMSO, 0.3 %), or hTNF-α (10 ng/mL in PBS containing 0.1 % BSA [1 % of culture media]) and/or IL-8 (10 or 100 ng/mL in PBS containing 0.1 % BSA [0.5 – 1 % of culture media]) together (or not) with PEA (3 or 10 mM in DMSO [0.3 %]) for 24 hours prior to harvesting for analysis. All treatments (hTNF-α, IL-8, PEA or vehicle) were added simultainously.

3. Quantitative Polymerase Chain Reaction (qPCR) (Paper III and IV)

3.1. RNA extraction

3.1.1 Cell lines RAW 264.7 cells, seeded and treated in 6-well plates, were washed with cold phosophate-buffered saline (PBS) prior to addition of cold lysis/binding buffer (100 µM tris (hydroxymethyl) aminomethane (Tris), 500 µM LiCl, 1 % lithium dodecyl sulphate, 5 mM dithiothreitol, pH 7.5) and frozen in - 80 °C. mRNA was extracted using DYNABEADS® mRNA direct kits (Thermo Fisher Scientific) according to the manufacturer’s manual. Briefly, lysates are incubated with poly- T labelled magnetic beads and separated from sample lysate using a magnet during a series of washing steps at room temperature. In the final step, mRNA is released from the beads into 10 mM Tris buffer containing 1mM ethylenediaminetetraacetic acid pH 8 (EDTA) during a 2 min incubation at 80 °C. The extracted mRNA was stored in -20 °C prior to reverse transcription.

3.1.2 Oral lichen planus biopsies Tissue samples were collected by taking 4 mm punch biopsies from healthy volunteers and OLP patients. The OLP biopsies were taken from a reticular area. None of the participants were currently using immunosuppressants or NSAIDs at the time of the study. Samples were of varying size and some were embedded in Tissue-Tec® and some frozen directly in the vial. Collected samples resulted in two cohorts, one used for qPCR and one for PG/NAE analysis with UPLC-MS/MS (the latter is described in full in Manuscript IV). For the qPCR cohort, samples from 15 OLP patients (10 females, 5 males; age range 44-81 years, median 64 years) and 15 controls (12 females, 3 males; age range 39-73 years, median 61 years) were obtained. Biopsies were taken out of -80 °C storage in sets of 3-4 samples to thaw. If the samples were embedded in Tissue-Tec®, two scalpels were used to cut the tissue out as it was thawing. Whole samples were cut in 2x2 mm pieces before placed in tubes from the Precellys® 2 mL Soft Tissue Homogenizing Ceramic Beads Kit (CK14, Bertin Instruments). 600 µL RLT Buffer Plus was then added and tubes placed on ice. Samples were homogenized using a Precellys®24 tissue homogenizer. 600 µL of sample were used for the Qiagen Allprep® DNA/RNA/miRNA kit (Qiagen) that was then used according to the product manual. Extracted RNA was eluted once prior to quality and amount control in Nano Drop™ Lite (Thermo Fisher Scientific) spectrophotometer followed by storage at -20 °C.

3.2 Complementary DNA (cDNA) conversion

3.2.1 Cell lines The Nano Drop™ Lite (Thermo Fisher Scientific) spectrophotometer was used to assess the concentration and the purity of the extracted mRNA measuring absorbance at 260 nm. A ratio of 2.0 between 260/280 nm wavelengths is considered good RNA quality with minor to none solvent or protein contamination. Since only mRNA was extracted using DYNABEADS®, 5 ng/µl mRNA was diluted for reverse transcription of mRNA to cDNA. High-capacity cDNA reverse Transcription Kit (Applied BiosystemsTM) was used to perform this process. Diluted mRNA samples were mixed with master mix containing deoxynucleotides (dNTP), random short primers, reverse transcriptase, RNase inhibitor, RT-buffer and nuclease free water. Samples were then placed in the LifeECO thermal cycler (BIOER) using the following conditions: 25 °C for 10 minutes, 37 °C for 120 minutes, following termination at 85 °C for 5 minutes followed by a temperature drop to 4 °C until collection. Dilution of cDNA at 1:10 (in 10 mM Tris, 1 mM EDTA-buffer pH 8.0) allows for processing more genes and decreased risk of RT-buffer in samples inhibiting the PCR-reaction.

3.2.2 OLP biopsies Since complete RNA was extracted from OLP biopsies, 1 µg/µl is the recommended amount to use for most reverse transcription kits. GoScript™ Reverse Transcription Mix, Oligo(dT) (Promega) was used to convert RNA to cDNA. Samples were diluted to contain 15 ng/µl (150 ng total RNA) instead of 100 ng/µl (1 µg) as recommended by the manufacturer, simply because reverse transcription kits are very sensitive (the GoScript™ kit has a performance range from 10pg-1µg) and we wanted to be able to use all samples and some were small and thus contained less material. The obvious disadvantage is however that the smaller volume of cDNA would result in a later detection of SYBR green signal, and hence genes that are of low abundancy in the sample will have to be run undiluted to get a signal within a stable cycle range (cycle threshold (Ct’s) values over 38 are considered weak reactions indicative of minimal amounts of target nucleic acid which could represent environmental contamination). The GoScript™ Reverse Transcription Mix, Oligo(dT) kit (Promega) contains an enzyme mix, reaction buffer with Oligo(dT) and nuclease free water. Oligo(dT) will only retrotranscribe RNA with a polyA tail (most mRNA will thus be transcribed). The mix is added to the diluted amount of RNA prior to loading in the thermal cycler. A RNA denaturing step incubating the samples in 70 °C for 5 minutes followed by 5-minute chill on ice was performed in order to denature RNA with high tendency of forming secondary structures (>60 % GC content). The following conditions were then used in the thermal cycler: 25 °C for 5

minutes, 42 °C for 60 minutes, 70 °C for 15 minutes, followed by 4 °C until collection of samples from the thermal cycler.

3.3 Reverse Transcription Real-time qPCR (RT-qPCR) A master mix containing SYBR green (reporter dye), nuclease free water and primers were added to samples of cDNA (diluted 1:10, or undiluted). Quantitative real-time amplification of mRNA in samples were performed in an Eco™ Real- Time PCR System (Illumina) using the following protocol (same for cell lines and biopsies): first the activation phase, heating to 95 °C; and amplification phase, 95 °C for 10 sec then 60 °C for 30 sec in each cycle (45 cycles); followed by a melting phase ranging from 55 °C to 95 °C to assess sample quality and single product amplification. No template controls were included on each plate run in the Illumina Eco. Primers were designed to be intron skipping in order to avoid DNA- amplification. For primer sequences and efficiencies see Table 2.

For data analysis the Ct was set to 0.299 - 0.301 fluorescent units in the Illumina Eco software 4.0 prior to exporting Ct data to Microsoft Excel format. Then mRNA expression was calculated as the relative expression to the reference or “housekeeping gene” of our choice (RPL19, see Table 2). Data is presented both as 2-ΔΔCt and ΔCt in our papers, allowing for both interpretation of fold change of target mRNA relative to control mRNA as well as providing information on the relative levels of mRNA for the genes of interest.

∆�� (��) = �� (��) − ��(��)

∆∆�� = �� ∆��(�� ) − �� ∆�� (�� )

2∆∆ = �� ℎ��

Thus one unit decrease in ΔCt is equal to a doubling of the mRNA amount. The ΔΔCt method also assumes equal amplification efficiencies between reference gene and target gene to be approximately equal.

The obvious advantage of a method as sensitive and fast as qPCR is that it is able to provide large amounts of data. It should however be kept in mind that the method, as any other, also comes with limitations. First, qPCR measures the amplification of genetic material, RNA (or mRNA). An upregulation in mRNA does not necessarily translate to increased protein levels of that specific gene, since mRNA is not the sole regulator of protein translation in the cell. The choice of reference gene is crucial to reliable results, since a reference gene with stable expression regardless of treatment is wanted. There are several commonly suggested stable reference genes, RPL19 being one of them, but there is no ideal

Table 2. Primer sequences targeting mRNA used in RT qPCR. Primer efficiencies were calculated using six sample serial dilutions over a fifty-fold dilution range. These efficiencies were not used to correct Ct values. Sequences were designed to be intron skipping avoiding DNA-amplification.

Mouse primers

Gene (product) Forward primer (5’ to 3’) Reverse primer (5’ to 3’) Efficiency

Rpl19 (Ribosomal TGA CCT GGA TGA GAA CTG TGA TAC ATA TGG 97 protein 19) GGA TGA G CGG TCA ATC TGA ATA CCG AAA GAG ATG GAG TTG GGC TGG Ptgs1 (COX1) 71 GTT TGG CTT G CAC TT AGA TTC CCT CCG GTG CCC TTC TCA CTG GCT Ptgs2 (COX2) 99 TTT G TAT GTA G GGG ATG TCA CAC AAT CAG GTC GTG TTC ACA Ppara (PPAR-a) 95 GCA ATT GGT AAG AGA GTA GGA GTA TCA CAT CAG CAG CGT TTA Faah (FAAH) 95 GGG AGT G AGT CG ATT ATG ACC ATT GGA CGC TCA TCA CTG TAG Naaa (NAAA) 99 AGC CTG CA TAT AAA TTG TGT AG Napepld (NAPE- GAC CCA GAA GAT GCT CTG GCG GCT CTA GGT 101 PLD) GTA AGG AAT G CTT GTG CAA GTG TCT CCA GGT CAA AGG TTT Il1b (Il-1b) GAA G 98 GGA AG

Tumor necrosis GGG TGT TCA TCC ATT GTT GGA CCC TGA GCC 93 factor (TNF-a) CTC TAC C ATA ATC Alox15 TTT GAA GCG GAT TTC GGT GGG GTA GAC CCA (Arachidonate 15- 95 TTC CTT GTT TT lipoxygenase)

Human primers

NAPEPLD (NAPE- ACT GGT TAT TGC CCT AAT CCT TAC AGC TTC 99 PLD) GCT TT TTC TGG G AGC AGG CAG ATG AAA ACC AGA AGG GCA GGA PTGS2 (COX-2) 93 TAC CAG TAC A ATG GAG CGT GGT TCC AGG CTG AGG TTT GCT NAAA (NAAA) 99 GAG TT TGT CCT CAC ACG CTG GTT CCC GGG TCC ACG AAA TCA FAAH (FAAH) 99 TTC TT CCT TTG A RPL19 (Ribosomal CAC ATC CAC AAG CTG CTT GCG TGC TTC CTT 99 protein 19) AAG GCA GGT CT

reference gene (Rebouças et al. 2013). In addition, due to the extreme sensitivity of qPCR, it is prone to some obvious problems regarding the many pipetting steps, and contamination with solvents or foreign genetic material is easy, yet these can be minimized using good laboratory practice and materials.

4. Cyclooxygenase activity assay (Paper III) In order to investigate if PEA had any direct effects upon COX enzyme activity we utilized the Oxygraph+ system (Hansatech Instruments, King’s Lynn, U.K.). The Oxygraph system features a disc within an electrode chamber that measures dissolved oxygen in liquid phase samples. Hence, this system can be used to measure the amount of oxygen consumed in the cyclooxygenase reaction of COX- enzymes upon addition of substrates arachidonic acid (AA) or 2- arachidonoylglycerol (2-AG). AA, the precursor of prostaglandins, is a substrate for both COX enzymes whereas 2-AG, the precursor for prostaglandin glyceryl esters (Kozak et al. 2000) can only act as a substrate for COX-2, see Figure 6 for the reaction steps. The more oxygen that disappears from the chamber for a given unit of time, the greater the enzymatic activity.

2 O2

H2O Arachidonic acid

PGG2

PGH2

Figure 6. The enzymatic activity of cyclooxygenase 2 upon arachidonic acid and prostaglandins G2 and H2 as example. The first reaction step, when COX-enzymes convert arachidonic acid to an unstable intermediate PGG2, is where the Oxygraph measures the loss of oxygen. PGHa is then further metabolised by the COX-enzymes forming PGH2 that in turn is converted into prostaglandins by their individual prostanoid synthases.

The assay was performed as previously described (Meade et al. 1993) with minor modifications (Onnis et al. 2010). In short, samples of PEA and controls were diluted in DMSO and then prepared in a buffer containing 0.1 mol/L Tris-HCl pH 7.4–7.6, 1 mmol/L haematin, 2 mmol/L phenol and finally 2 or 10 µmol/L enzyme substrate (AA or 2-AG) with a final assay volume of 2 mL in the chamber. A baseline was established for 5 min before initiation of the reaction by addition of 200 units of ovine COX-1 or human recombinant COX-2. The assay is calibrated to ambient temperature and local air pressure that unfortunately comes with obvious potential errors. Thus, we perform these experiments in triplicates for each replicate that is then performed on different days.

The advantage (but also limitation) of this method is the ability to study enzyme activity without interference from other biological processes. COX enzyme activity is more frequently measured using commercial kits, but these do not allow for the use of alternate substrates.

5. Statistics The statistical analyses used in this thesis and published papers were undertaken in R (R Core Team, 2016-2019) and GraphPad Prism for Macintosh versions 6h, 7a, 8.4 (Graphpad Software Inc., San Diego, CA, USA). Most of the analyses used are standard, but two methods are discussed below since they may be less familiar:

5.1 Resampling techniques (Paper II and III) Resampling techniques are used to draw new samples from a given sample or population. There are two methods used to do this, permutation test or bootstrapping, and they can be used in a number of ways, such as, for example, the determination of confidence intervals when the underlying distribution of the data is not known. In order to aid the reader, two simple examples using these techniques are given below, namely the permutation test and the determination of percentile confidence intervals.

5.1.1 Permutation test (Paper II) Consider a study investigating the mean change in VAS score when a new pain medication is tested against a placebo control. First, the chosen population is randomly assigned to two groups: treatment and control. Data is collected at a certain timepoint and the response value (decreased pain) is measured using a VAS scale. As an example, let us say the scores for the treatment group were 3.8, 5.2, 4.6, 3.3, 3.6 (mean 4.1) and the placebo control were 6.8, 5.1, 6.3, 6.9, 5.9 (mean 6.2) giving a difference of means of 2.1.

Now for distinguishing measure of the permutation test; all individuals (and their VAS scores) are shuffled (permuted) between groups (to generate a distribution curve under the null hypothesis. If the null hypothesis is true, it does not matter if the VAS-scores are mixed from the control- and treatment groups, since they belong to the same population. So an example permutation would be “treated” 3.8, 6.8, 6.3, 4.6, 3.6 (mean 5.02), “placebo” 5.2, 3.3, 5.1, 6.9, 5.9 (mean 5.28, numbers in italics are the permuted numbers). This process in then repeated over and over again, either until all permutations are tested (252 in this case), or, up to a given number, say 10000 permutations for larger datasets. In this way, it is possible to build an approximation of the distribution of mean differences. This distribution represents all possible test statistic values that could have been seen under the null hypothesis. Calculating the p-value for this permutation test is done by counting the number of test statistics (mean VAS) that are the same or more extreme than our initial test statistic and then dividing that number with the total number of permutation tests done. E.g. if 8 of the mean VAS scores were more extreme than our initial score and we did 252 permutations, the p-value is 8/252 = 0.03 = 3 %, meaning there was only an 3 % probability of obtaining the initial difference in VAS scores if the treatment is in fact ineffective. In the example above, the p value using the permutation test in the coin package for R was 0.012.

In paper II (in the supplement section), we have used the permutation technique to generate all possible datasets where three cases were excluded, in order to give an idea as to the robustness of the values returned by the tests used.

5.1.2 Bootstrapped confidence intervals (Paper III) Instead of choosing each individual mean from both groups only once, bootstrapping creates new samples with replacement, that is the same mean can be randomly chosen more than once to that sample (still keeping the same sample size as the original). For the dataset above, let us say that the cases were paired, so that the difference in VAS values between placebo and treated were 3.8, 2.1, 3.3, 3.9 and 2.9. A bootstrapped selection could be, for example, 3.8, 3.8, 3.8, 2.1 and 2.9. Once again, multiple bootstraps allow the generation of all possible values. For 1000 replications, the percentile 95 % confidence interval of the means bootstrapped means can be calculated (2.60-3.74). The benefits of bootstrapping are that it is conceptually easy to understand and it does not require assumptions about the sample distribution.

In paper (III) we used bootstrapped linear models (Hall and Wilson 1991) to assess the effects of PEA upon prostaglandin production by RAW 264.7 cells.

5.2 Conditional logit regression (Paper II) Logistic regression, like all regression analyses, are a predictive method estimating the relationship between dependent variables and one or more independent variable(s). Logit regression is the appropriate regression analysis to conduct when linear regression is not applicable due to the classification problem, where all answers to dependent variables are in categorical binary form i.e. yes/no. Hence it can answer research questions such as “How does the dose and treatment duration of NSAIDs change the probability of having an acute gastrointestinal bleed (yes vs.no)”. The logistic regression instead uses a sigmoid function of the data.

Conditional logistic regression (CLR) is a special case of logistic regression to be used when observations are not independent but are matched or grouped, and this needs to be taken into account (see Koletsi and Pandis 2017 for a nice example of a simple conditional regression). This is the case for the BWS data summarised in paper II.

Results

1. Attitudes and beliefs amongst final year medical students (Paper I)

1.1 Survey demographics Swedish medical students were invited to participate by email and Australian medical students were invited through an announcement on their online learning management system. Response rates were 24 % for Swedish students (80 out of 327 invites sent) and 27 % for Australian students (30 out of 110 reached though their platform). There was a considerable attrition rate throughout the completion of the survey (see Figure 7), with significantly more Swedish students choosing to discontinue the survey compared to Australian students (p<0.01, Fisher’s exact test on the absolute numbers). The difference in attrition could possibly be explained by the incentive given to Australian students, where they could enter a draw to win one of two gift cards worth 100 AUD. Studies have shown that incentives increase the response rate of surveys (Cobanoglu and Cobanoglu 2003, Smith et al. 2019).

The absolute values (taken from Figure 1 of Paper I) have been recalculated here as % of the number of students originally contacted. The four phases of the study are described in the Methods and in Paper I. BWSDCE – BWS discrete choice experiment.

Figure 7. Attrition rates throughout the study

No statistical differences were found between the two cohorts in regard to questions asked in the demographic section of the survey (except for future workplace, see Paper I). The majority of respondents were female, 25-29 years and only a few were considering pursuing general medicine as a medical specialty

(see Table 1 of Paper I). A majority from both student cohorts knew someone (relative or close friend) who suffers from chronic pain, and the majority found their treatment to be inadequate. One in six had experienced chronic pain themselves.

1.2 HC-PAIRS scores The attitudes and beliefs amongst final year medical students in Sweden and Australia were measured using HC-PAIRS. Swedish students had a significantly lower total score than Australian students, including factors 1 and 3 (Figure 8, note that scatterplots showing individual scores are shown in Supplementary Figure S1 in Paper I). These results indicate that Swedish medical students have less limiting beliefs about chronic pain patients, e.g. that chronic pain should not necessarily limit their functional performance.

Data are taken from Table 2 of Paper I.

**P<0.01, NSP>0.05, two-tailed t-tests with Welch’s correction for AUS unequal variances. At a SWE 5% false discovery rate, the critical value of p was 0.03.

Figure 8. HC-PAIRS total scores including dimension (factor) scores between Swedish and Australian students.

Our most negative scores were found with item 10 and 13. The statements for these items are “When their pain gets worse, chronic pain patients find it very hard to concentrate on anything else” and “Chronic pain patients find themselves frequently thinking about their pain and what it has done to their life” respectively. Although these factors can describe the cognitive burden of living with chronic pain, a generalized view as such can be potentially stigmatising.

On a factor level, differences in factor 3 and 4 were not statistically significant and the internal consistency for factor 4 is not clear (Rainville et al. 1995). We found effect sizes (Cohen’s d) for total score, factor 1 and 3 to be; 0.8, 0.86 and 0.77 respectively, which indicates that the differences we found between the student

cohorts to have large effect sizes. In order to control if the high attrition rate for the Swedish students had an impact on the HC-PAIRS scores we compared the students who completed the survey to the students not completing the survey and found no significant differences between these groups for total score or factors 1, 3 or 4 (p>0.36, two tailed t-test with Welch’s correction). Factor 2, social expectations, did however differ between these two groups where the score was higher (p=0.0098, two tailed t-test with Welch’s correction) for the Swedish students who did finish the survey compared to ones that did not. Considering if the demographic data in the final section of the survey had any impact on responders HC-PAIRS scores, we constructed linear models for all students from both cohorts for the data for Factors 1 and 3. Apart from a significant main effect of land, no significant differences (p>0.5) were found, indicating factors such as gender or knowing someone suffering from chronic pain did not contribute to differences in HC-PAIRS scores (see original supplementary Table S2 of Paper I).

1.3 Students’ thoughts about chronic pain education Through the open-ended questions in our survey, it was clear that final year medical students are knowledgeable about key terms in chronic pain management but they wanted more education on the topic. From these answers, the identified themes for each question are presented in Figure 8, but are given in more detail, with example quotes, in Table 3 of Paper I.

We concluded that pain assessment, the biopsychosocial model, pharmacological and non-pharmacological treatment, patient attentiveness, and treatment strategies were all considered important skills and knowledge for appropriate chronic pain management. There were some detectable differences between Australian and Swedish medical students, regarding “patient attentiveness”. Australian students tended to focus more on this compared to Swedish students, one Australian student wrote “…being able to determine what is important to the patient in terms of sedation vs pain relief”. Furthermore, the Swedish students more often mentioned specific specialities and knowledge as important tools for pain assessment such as: neurology, orthopaedics, physiology, psychology, and knowledge about different pain types.

Regarding pharmacological and non-pharmacological treatment, students expressed an attitude towards non-pharmacological treatment as being considered less valuable. One Australian student wrote “Adjunct therapies have not been taught very much. Treating the pain pharmacologically is important but also strategies to help the patient cope and improve function are valuable. The attitude towards these seemed negative (like they are a bit of hocus-pocus)”.

During your training is What do you believe What are your overall there something that are the important thoughts about could have been done areas of knowledge chronic pain to improve your and skills in chronic management competency in chronic pain management? education? pain management?

Patient attentiveness Needing more chronic pain Complex and important education Pain assessment Poor management and need Needing more patient of improvement Pharmacological treatment experience Satisfied with my education Non-pharmacological Needing more knowledge treatment on pharmacology, treatment plans and Biopsychosocial model expectations

Treatment strategies Needing more non- pharmacological treatment

Needing more focus on the biopsychosocial model

Figure 9. Themes identified from open-ended questions. The figure shows all identified themes for each open-ended question. For a more detailed description of themes and example quotes see Table 3 Paper I.

When asked if there was something during their training that could be improved, both student cohorts expressed the need for more chronic pain education. There were several suggestions to what topics needed more attention in their curriculum: the biopsychosocial model, pharmacological and non- pharmacological treatment plans, and seeing more chronic pain patients. Evidently the students were knowledgeable about important terms for chronic pain, however they expressed needing more of it such as exemplified by one Swedish student; “Yes, everything involves psychosocial factors and I think we need to learn how to deal with this better”. Gaining experience of working in multimodal teams were also often mentioned, as exemplified by this Australian student “Getting experience with how physiotherapists manage patients with chronic pain. Not just "treatment" but also education, encouragement and follow-up with other allied health professionals. Not just sitting in on "chronic pain clinics" where doctors hand out medications”.

Within the theme “Needing more knowledge on pharmacology, treatment plans and expectations”, there was a notable difference between Australian and Swedish students. Swedish students were looking to apply their knowledge in the clinic, requesting more information on guidelines and treatment plans, whilst the

Australian students sought for more basal pharmacology as exemplified by this quote: “pharmacology tutorials on all medications but including pain medications would have been highly beneficial”. Swedish students also tended to express the will to see more chronic pain patients. One Swedish student mentioned “handling chronic pain patients in primary care”, whilst Australian students wanted to learn more from fictive patient cases.

In the answers for the final question “What are your overall thoughts about chronic pain management education?”, some students also expressed satisfaction with their own chronic pain education. Many students considered chronic pain a very important topic that is difficult to treat due to its complexity, and they considered current chronic pain management being poor. Furthermore, many students again wanted to highlight the importance of improving their education in regard to chronic pain. For example one student wrote “Somewhat lacking in regards to practical, everyday treatment of patients”. There was a difference in answers between the student cohorts reflected in their curriculums, where Australian students are assigned different rotations during their education, whilst all Swedish students are assigned to the pain and rehabilitation clinic during their fourth year. Many Swedish students expressed joy of gaining that experience but again stressed the amount of chronic pain education as being poor. One Swedish student wrote “Rehabilitation medicine during the eighth semester gave insight and lectures about chronic pain and treatment during a few days, which is perhaps somewhat short seeing how extensive this problem is”. Many Australian students expressed gratitude for being assigned a specific rotation that gave them exposure to pain clinics; “I was lucky to do a week in the pain clinic at St Vincent’s Hospital during my psychiatry rotation in 3rd year. it was a great experience that all students should have”.

2. Factors affecting treatment choices made by Swedish GPs and final year medical students (Paper II) Through a BWS experiment with Australian and Swedish final year medical students and Swedish GPs, we elicited the most and least influential factors affecting chronic pain management choices for a new chronic pain patient typically encountered in primary care. We found that both student cohorts favoured typical pain assessment factors such as ‘patients’ pain description’, ‘average pain rating over the past week’, ‘previous treatment experience’ and ‘treatment history’. Factors most often chosen to be of least importance were ‘social support’, ‘treatment adherence’, ‘patient demographics’ and ‘voice and facial expression when describing their pain’ (see Figure 10 for the top 3 factors chosen as best or worst by each cohort).

There were some considerable differences observed between the student cohorts; Swedish students more often chose the factor ‘Your professional experience’ compared to Australian students, who indeed often placed this factor as the least important (see Table 3 for all cohort scores). Regarding pain rating factors, the Swedish students considered both pain rating factors (current/over the past week) as part of their most important, whilst Australian students were focused on the week-long pain rating and the ‘current pain rating’ factor was instead seen as one of the less important factors.

Table 3. Best and worst simple counts for each cohort. Each item was presented 5 times, hence maximum possible score per item (factor) was 150 for Swedish students, 105 for Australian students and 80 for Swedish GPs. NC, not chosen, i.e. total scores – (best+worst). Data taken from Tables 2-4 of Paper II (note that the total scores in Table 4 for the Swedish GPs is incorrectly given as 105).

Australian students Swedish GPs Swedish students n=30 n=21 n=16

Best Worst NC Best Worst NC Best Worst NC

1. Patients’ preferences 16 14 120 18 8 79 10 16 54 for treatment (a)

2. Patients’ pain history 39 5 106 45 0 60 22 3 55 (b)

3. Patients’ pain 110 1 39 45 3 57 45 0 35 description (c)

4. Patients’ social support 12 43 95 16 29 60 10 22 48 (d)

5. Your professional 16 13 121 6 34 65 26 3 51 experience (e)

6. Patients’ voice or facial expressions when 6 76 68 1 56 48 3 32 45 describing their pain (f)

7. Patients’ demographics 3 111 36 1 51 53 0 58 22 (g)

8. Patients’ previous 47 10 93 50 4 51 24 4 52 treatment experiences (h)

9. Patients’ average pain rating over the past week 40 18 92 24 3 78 14 12 54 (i)

10. Patients’ current pain 29 14 107 8 23 74 14 13 53 rating (j)

11. Patients’ history of 12 25 113 17 20 68 8 13 59 treatment adherence (k)

100 100

50 50 Response % Response Response % Response % Response Response % Not chosen Not chosen 0 0 Worst ) Worst ) g h Data from Paper II.

Patients’ Best te. Best a r their pain (f) pain their Patients’ pain experience (e) Australian students Australian students description (c) Your Your professional demographics ( Patients’ previous Patients’ pain history (b) Patients ‘demographics (g) Patients’ pain description (c) Patients’ voice or facial Your professional experience (e) Australian students - Top 3 Factors chosen as best Patients’ historypain (b) Australian students - Top 3 Factors chosen as worst treatment experiences ( experiences treatment Patients’ voice or facial expressions when describing their pain (f) expressions when describing 100 100 Patients’ previous treatment experiences (h) 50 50 Response % Response Response % Response % Response Response % Not chosen Not chosen students students 0 0 ) ) ) g i h Worst . Aggregate scores as % of the total response response total the of % as scores . Aggregate Worst Patients’ support (d) (d) support Best their pain (f) pain their Swedish Swedish Best Patients’ pain Patients’ social description (c) demographics ( the past week ( week past the Patients’ previous Patients’ social support (d) Patients ‘demographics (g) by all cohorts

Patients’ pain description (c) Patients’ voice or facial Swedish students - Top 3 Factors chosen as best Patients’ rating pain over Patients’ voice or facial expressions when describing their pain (f) treatment experiences ( experiences treatment worst

expressions when describing Swedish students - Top 3 Factors chosen as worst 100 or B) Patients’ previous treatment experiences (h)

100 best Patients’ average pain rating over the past week (i) A) s s 50 50 GP GP Not chosen Response % Response Response % Not chosen Response % Response Response % chosen as chosen

Worst 0 Worst ) 0 actors h f Swedish Swedish ( ) g Best op op 3 Best T Patients’ . GPs - Top 3 Factors chosen as best Patients’ pain support (d) (d) support description (c) 10 experience (e) GPs - Top 3 Factors chosen as worst Your Your professional Patients’ social Patients’ previous for treatment (a) (a) treatment for Patients’ pain description (c) 42 demographics ( A B treatment experiences experiences treatment Patients’ social support (d) Patients ‘demographics (g) Your professional experience (e) Patients’ preferences Figure Patients preferences for treatment (a) Patients’ previous treatment experiences (h)

In general, Swedish GPs considered the same factors most and least important as the Swedish medical students. The differences being ‘your professional experience’ where, not surprisingly, the GPs considered this factor more often. It was indeed the second most favoured factor by the GPs (see Figure 9). Another difference between GPs and student cohorts was the use of factors utilizing pain ratings where GPs seldom considered these as most or least important. Factors seldom chosen as either best or worst by all cohorts where factors such as ‘Patients’ preferences for treatment’, ‘Patients’ voice or facial expressions when describing their pain’, ‘Patient history of treatment adherence’ (see Figure 11).

Australian students (n=21) Swedish students (n=30) Swedish GPs (n=16)

1.0

0.5

0.0

-0.5

Individual mean standardised B-W mean standardised Individual -1.0 (h) (b) (c) (i) (a) (k) (d) (j) (e) (g) (f)

(h) Patients’ previous treatment experiences (d) Patients’ social support

(b) Patients’ pain history (j) Patients’ current pain rating

(i) Patients’ average pain rating over the past week (g) Patients’ demographics

(a) Patients preferences for treatment (f) Patients’ voice or facial expressions...

(k) Patients’ history of treatment adherence

Figure 11. Mean standardized best-worst individual scores for all cohorts ordered according to the Australian students from best to worst.

Given that the sample sizes are relatively small, we wanted to assess the robustness of our analyses. We did this by investigating all the possible combinations of the data (4040 for the Swedish students, 1330 for the Australian students and 560 for the Australian GPs) for the three cohorts where the values returned by three of the respondents were excluded case-wise. The data indicated that the spread in B-W scores and exp logit coefficients is acceptable for all three groups (Figures are found in Paper II, Supplementary Material File 2).

Regarding differences between groups, chi square tests performed on the responses best, worst or not chosen (see Table 3 for raw data) allowed for all factors except ‘c’ (patients’ pain description) and ‘g’ (patients’ demographics) (due to group distributions) to be compared between groups. Factors ‘b’, ‘e’, ‘i’ and ‘j’ had chi-square p values that were smaller than the critical value of p of 0.022 (assuming a 5 % false discovery rate). Factor ‘a’ had a p value of 0.037 and the other factors (‘d’,’f’,’h’) had p values > 0.05. Post hoc tests indicated that for the Australian students, factors ‘b’ (patients’ pain history) and ‘i’ (patients’ average pain rating over the past week) were significantly different from other cohorts. Regarding factor ‘e’ (your professional experience) there was a difference between all cohorts, and for ‘j’ (patients’ current pain rating) Swedish and Australian students differed significantly.

In the conditional logit regression (clogit) models (see Table 4) where factor ‘d’ was chosen as reference and all other factors were hence compared relative to this factor. In theory, any factor can be chosen as reference, but we chose factor ‘d’ as the relative best-worst responses were very similar for the three groups (see Figure 2 Paper II). The best-worst scores and clogit are expected to have be highly correlated, and this was the case in our data for all three cohorts (See Paper II, Supplementary Material File 2).

We further confirmed the quality of our collected data by conducting permutation tests on the best-worst data followed by ‘leave three out’ approach (a type of cross- validation on the observations) (Figures are found in Paper II, Supplementary Material File 2). Finally, differences between groups were also presented in a simple linear regression of the exp logit coefficients where differences can be seen when factors are found outside the 95% confidence interval (see Paper II Figure 4), these results were also in line with the results from the Chi square data.

Table 4. clogit analysis where d was chosen as reference. Data from Tables 2-4 of Paper II.

Swedish students n=30 Australian students n=21 Swedish GPs n=16

Exp CI exp Exp CI exp Exp CI exp Factor Coeff p Coeff p Coeff p (coeff) (coeff) (coeff) (coeff) (coeff) (coeff)

1.47- 1.29- 0.71- a 0.84 2.32 <0.001 0.82 2.26 >0.005 0.25 1.29 <0.5 3.65 3.96 2.35

3.12- 3.78- 2.15- b 1.60 4.95 <0.001 1.86 6.45 <0.001 1.38 3.96 <0.001 7.87 11.0 7.29

16.1- 3.41- 5.97- c 3.27 26.3 <0.001 1.77 5.85 <0.001 2.40 11.1 <0.001 43.0 10.0 20.5

1.60- - 0.36- 2.65- e 0.93 2.54 <0.001 0.62 0.070 1.59 4.90 <0.001 4.03 0.48 1.04 9.08

- 0.27- - 0.17- - 0.28- f 0.42 <0.001 0.29 <0.001 0.51 0.024 0.87 0.65 1.24 0.49 0.68 0.91

- 0.11- - 0.19- - 0.089- g 0.18 <0.001 0.32 <0.001 0.17 <0.001 1.72 0.29 1.13 0.55 1.79 0.31

3.28- 3.92- 2.17- h 1.65 5.18 <0.001 1.90 6.71 <0.001 1.38 3.98 <0.001 8.18 11.5 7.27

2.26- 1.78- 0.93- i 1.28 3.60 <0.001 1.11 3.04 <0.001 0.53 1.70 >0.05 5.72 5.22 3.09

1.82- - 0.48- 0.86- j 1.07 2.91 <0.001 0.83 >0.5 0.47 1.61 0.14 4.66 0.18 1.44 2.99

1.09- 0.84- 0.79- k 0.53 1.70 0.019 0.34 1.41 >0.2 0.35 1.41 0.25 2.64 2.37 2.54

3. Does PEA have direct effects upon COX-2? (Paper III) In paper III, our main goal was to investigate if PEA affects the expression and/or catalytic activity of COX-2. We chose RAW 264.7 cells as our model for this investigation given the potential anti-inflammatory role of PEA on macrophages (see Zhu et al. 2011, Di Cesare Mannelli et al. 2013, Redlich et al. 2014, Li et al. 2018) and given the dramatic up-regulation of COX-2 produced by activators such as LPS and/or IFN-g in this cell line (see Zhu et al. 2011). Our approach was to investigate the effect of PEA upon LPS + IFN-g induced up-regulation of COX- 2 at the mRNA and protein levels, as well as on the levels of COX-2-derived prostaglandins and other oxylipins in RAW 264.7 cells. Lastly, we also wanted to to determine the direct effects of PEA upon the catalytic properties of COX-2 in a cell-free system using an Oxygraph (see Methods, section 4).

3.1 Effects of PEA in RAW 254.7 cells Cells were treated with LPS (0.1 µg/mL) + IFN-g (100 U/mL) for either 30 min or 24 h in the absence or presence of PEA (10 µmol/L) before determination of mRNA for Ptgs2 (which codes for COX-2), COX-2 protein or levels of COX-2 metabolites (prostaglandins) and other oxylipins. The main findings are summarised in Figure 12 and described in more detail below.

3.1.1 mRNA levels of Ptgs2, Faah and Naaa After 24 h treatment with LPS and IFN-g, Ptgs2 levels were increased by ~ 1400- fold. However, no significant effects of PEA upon Ptgs2 were seen at the mRNA level (see Figure 12 A).

In order to investigate how PEA is degraded in RAW 264.7 cells, we considered changes in expression of genes coding for FAAH and NAAA. In the absence of PEA at the 30 min timepoint, the mean Ct for Faah was 14.7 and 7.5 for Naaa, which can be translated into a ~ 150 fold difference in favour of Naaa (14.7 - 7.5 = 7.2, 2-7,2 ~ 147). However, after 24 h treatment with LPS and IFN-g, this relationship was changed, Faah levels increased and Naaa decreased, although Naaa was still more abundant than Faah. NAAA is known to be abundant in macrophages, be more effective in acidic environments such as during inflammation, and it prefers PEA as a substrate over other NAEs (Ueda et al. 2001, Tsuboi et al. 2005). In order to investigate the relative contributions of FAAH and NAAA to the hydrolysis of PEA, [3H]PEA (10 µM) was added to intact RAW 264.7 cells and the hydrolysis was determined following prior preincubation in the absence or presence of selective inhibitors of FAAH (URB597, 1 µM), NAAA (pentadecylamine, 30 µM, or both. For the LPS + IFN-g treated cells, the mean hydrolysis rates (as % of added, N=4 with SD values

shown) were; vehicle 0.51±0.05, URB597 0.29±0.06, pentadecylamine 0.22±0.05, and URB597 + pentadecylamine 0.12±0.02 (data from Figure 4 of Paper III). Thus, FAAH and NAAA contribute approximately equally to the hydrolysis of 10 µM exogenously added PEA despite the large difference in their relative mRNA levels. In a subsequent study, we investigated the hydrolysis of PEA in human T84 colon carcinoma cells and found that the hydrolysis of PEA was brought about essentially by FAAH alone, despite similar levels of Faah and Naaa (Alhouayek et al. 2019a). These data suggest that although Naaa prefers PEA over AEA as a substrate (Ueda et al. 2001), FAAH has a higher PEA hydrolytic capacity than NAAA.

B) COX-2 protein levels

25.6

A) Ptgs2 mRNA levels, 24h 6.4

-3 1.6 LPS+IFNγ 4096

0.4 g/mg protein) log scale 1024 Unstimulated control = 1 0 µ

0.1 LPS + IFN 256

γ COX-2 ( ) 3 0.025 64

Rpl19 PEA Control Vehicle Ct ( 6 16 Δ

4 C) PGD2 PGE2 20 9 0.6

1 [PGE 15 2

0.4 ] (pmol/well) 12 10 ] (pmol/well) 2 PEA PEA 0.2 5

Control Vehicle [PGD

0 0.0 0 3 10 0 3 10 [PEA] (µM) Figure 12. Effects of PEA upon A) Ptgs2 mRNA levels, B) COX-2 protein levels, and C) PGD2 and PGE2 levels in RAW 264.7 cell, all after 24 h incubation. The figures are taken or redrawn from Figures. 1B, 1C and 3A of Paper III. Mean values are represented by the bars. For ∆Ct values, a change of -1 represents a doubling and a change of +1 represents a halving in mRNA content. Left y-axes give the absolute ∆Ct values whilst the right y-axes give the relative expression for the mean control.

After Paper III was published, the author performed additional experiments using the same protocol but using another mouse macrophage cell line, J774.2,

in order to see whether the same pattern of mRNA effects for Ptgs2, Faah and Naaa was seen. The data are summarised in Figure 13. Faah-levels in this cell line were very low (DCt with respect to Rpl19 as reference gene ~17) and was therefore not analysed. However, genes coding for the PEA-synthesising enzyme NAPE-PLD (Napepld), the cellular target for LPS; TLR4 (Tlr4) and cytokines IL- 1b (Il1b) and TNF-a (Tnfa) were analysed. Similar upregulation of Ptgs2 was seen after stimulation with LPS and IFN-g, and this was matched with increases of Il1b and Tnfa. A decrease in Napepld expression was also noted. Naaa levels were not changed, but this may reflect the shorter exposure time (max. 6 h) used in these experiments compared to the RAW 264.7 experiments. PEA treatment did not affect the gene expression levels for any of the genes.

3.1.2 COX-2 protein levels In order to investigate COX-2 protein levels we used a commercial ELISA kit. Due to differences in cell density required by the manufacturer, we could not use the same timepoint of 24 hours as a discoloration of the media occurred and the experiment was stopped at 20 hours. We could however demonstrate the expected increase in COX-2 protein levels after 20-hours incubation with LPS and IFN-g (see Figure 12). There was no significant effect of PEA upon COX-2 protein levels in the LPS and IFN-g treated cells.

3.1.3 Prostaglandin and other oxylipin levels Cells were treated with LPS + IFN-g for 24 h prior to harvesting and measurement of oxylipin levels using ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). In the experiments, PEA (0, 3 or 10 µM) was present either for the final 30 min or for the entire 24 h period. The 24h treatment with 10 µM PEA reduced levels of PGD2 (35 %), PGE2 (37 %) as well as of 11- and 15-HETE (48 % and 33 %, respectively) in RAW 264.7 cells. No significant reduction was seen with 12-HETE or of the linoleic acid derivatives 9- and 13-HODE (see Figure 12 above and Figure 3 of Paper III).

3.4 Effects of PEA upon the catalytic activity of COX-2 The decreased levels of PGs, 11-and 15-HETE (which can be synthesised by COX- 2, Hecker et al. (1987), Xiao et al. (1997), Giménez-Bastida et al. (2019)), caused by PEA treatment without concomitant effects upon COX-2 expression, raise the possibility that PEA affects directly the catalytic activity of COX-2. This effect on COX-2 has been seen with long-chain fatty acids such as palmitic acid, oleic acid and stearic acid (Yuan et al. 2009, Dong et al. 2016). We thus investigated whether PEA in concentrations of 1, 3 and 10 µmol/L, had any effect on COX-2

unstimulated 2h control = 1 values

unstimulated 2h control = 1 p 0.26 0.01563 0.0625 4 1 0.5 0.125 0.25 4 2 1 6 h Naaa, no - + - + 6 h - + - + α and treated for 2, 4 and

h 4 h 4 h Tnf - + - + - + - + Napepld 2 h 2 h - + - + - + - +

7 8 9 1 3 5 7 9

10 11 12 Δ

) Rpl19 ( Ct

Δ ) Rpl19 ( Ct well plates for 24 PEA LPS PEA LPS - in in 24 unstimulated 2h control = 1 unstimulated 2h control = 1 0.25 0.0625 16 4 1 atterplots for 6 separate experiments. Three way ANOVA ANOVA way Three experiments.separate 6 foratterplots 0.5 0.125 0.25 4 2 1 6 h 6 h - + - + - + - + Shown are sc are Shown . β 4 h 4 h Il1 Naaa - + - + - + - + of PEA per se or as time x LPS x PEA were not significant. For

2 h 2 h - + - + - + - +

7 8 9

10 11 12

Δ ) Rpl19

Ct ( Ct 0 2 4 6 8

PEA LPS Δ ) Rpl19 ( Ct PEA LPS

unstimulated 2h control = 1 unstimulated 2h control = 1 0.5 0.125 0.25 2 1 0.0625 0.25 16 4 1 package for R were used to determine significant main effects and interactions. For all the genes except mol/L PEA mol/L µ 10 and/or U/mL) (100

g - 6 h s2, Naaa, Napepld, Trl4, Il1b and Tnfa in J774 cells cultured 6 h - + - + stats - - + + - + - + 4 h 4 h Tlr4 - + - + - - + + - + - + Ptgs2 levels levels of Ptg

for for time x LPS was but <0.002, the effects A 2 h 2 h - + - + - - + + - + - + mRN

p value

function function aov in the

8 9 0 2 4 6 8

10 11 12 Δ ) Rpl19

Ct ( Ct nificant. L. Rankin, unpublished data. Δ ) Rpl19 ( Ct PEA PEA LPS LPS

Figure 13. IFN + µ g/mL) (0.1 LPS. with h 6 using the Naaa, the were sig

enzymatic activity by measuring the oxygen consumption during metabolism of its substrates AA and 2-AG.

Figure 14 A shows an example with 10 µM AA as a substrate, and B-C show the values of the areas under the oxygen consumption curves over the first 120 s after addition of the substrate as a measure of total PG production. No significant effects of PEA upon the AUC0-120 values were found with any of the concentrations or substrates used.

A) AA (10µM) B) 2-AG (2 µmol/L) C) 2-AG (10 µmol/L)

0.0

450 1300 0µM 1200 -1.6 1µM 400

mol/L 3µM 1100 µ ] 2 mol.s/L) 10µM 350 mol.s/L) 1000 µ µ

[O -3.2 Δ 900 AUC ( AUC 300 ( AUC 800 -4.8 0 30 60 90 120 250 700 M M M M M M M M µ µ µ µ µ µ µ µ Time (s) 0 1 3 0 1 3 10 10

PEA concentration PEA concentration

Figure 14. Effects of PEA upon the activity of human recombinant COX-2 (A) Means ± S.E.M for cycloxygenation of 10 µM AA, N = 9; (B) Mean AUC (0-120 s, with 95 % CI) for cycloxygenation of 2 µM 2-AG, N = 6; and (C) Mean AUC (0-120 s, with 95 % CI) for cycloxygenation of 10 µM 2-AG, N = 6. Values represent the change in oxygen utilisation following addition of enzyme to the oxygen electrode chamber.

4. The levels of PEA, its synthesizing, and hydrolyzing enzymes in OLP-patients relative to the levels of COX-2 and its prostaglandin products (Manuscript IV) In paper IV, the aim was to investigate the relationship between PEA synthesising (NAPE-PLD) and PEA hydrolysing enzymes (FAAH, NAAH) relative to prostaglandins in OLP-patients. Our approach was to use RT-qPCR for mRNA analysis in biopsies from OLP-patients and healthy controls. Furthermore, we used the method of UPLC-MS/MS, to measure levels of NAEs and PGs in biopsies from another cohort of OLP-patients and controls. Finally, we investigated whether commercial cell lines, SCC-25 and CAL 27 (both human tongue squamous cell carcinoma) could be used as models for the disease.

4.1 mRNA levels of NAPE, PTGS2, FAAH and NAAA in biopsies from OLP patients and healthy controls Biopsies from 15 OLP patients and 15 controls were obtained for the study. After exclusion of samples where the available amount of tissue was too small, sample sizes were N = 13-14 for each group of the cohort. We found, in line with the literature, that PTGS2 was upregulated compared to the control group, resulting in a four-fold increase in PTGS2 expression for OLP patients (mean ∆Ct 10.79, 95% CI 9.60-11.98, N=13, and 12.86, 95% CL 12.01-13.71, N=13 respectively, P=0.0056, Welch’s t-test) (Lysitsa et al. 2008, Abdel Hay et al. 2012, Danielsson et al. 2012, Chankong et al. 2016) (See Figure 15 for ∆Ct and p values of all investigated genes).

We found no significant differences in FAAH or NAAA mRNA levels between OLP patients and controls, but consistent with previous data from tongue biopsies (Alhouayek et al. 2019b), we noted that the expression of FAAH was lower than that of NAAA. A significant increase in NAPEPLD levels was seen in OLP compared to controls (mean difference in ∆Ct -1.2, corresponding to a 2-fold increase) However, the p value of 0.037 was above the critical value of p = 0.0125 assuming a 5 % false discovery rate.

In order to assess the relative difference and balance in NAEs relative to the prostaglandin producing enzyme COX-2, we also compared the expression for genes coding for the PEA synthetic enzyme NAPE-PLD and COX-2 with each other (instead of the reference gene RPL19). Here, the mean (SD), N=13 difference between the controls and the OLP patients, 1.10, corresponds to roughly a doubling in PTGS2 expression relative to NAPEPLD (control 2.39 (1.08) and OLP 1.29 (1.40) (P=0.034, Welch’s t-test).

Relative expression (Mean control = 1) PTGS2Ptgs2 NAPEPLDNapepld Relative expression (Mean control = 1) p = 0.0056 p = 0.037 6 6 16 64 8 8 4 ) ) 16 10 10 1 Rpl19 Rpl19 4 12 12 Ct ( Ct (

Δ 0.25

Δ 1 14 14 0.025 0.06 16 16 CTL OLP CTL OLP Relative expression (Mean control = 1) FAAHFaah NAAANaaa Relative expression (Mean control = 1) p = 0.24 p = 0.74

4 16 1 16 6 3

) 4 ) 4 8 1 5 Rpl19 Rpl19 1 10 7 Ct ( 0.25 Ct ( Δ Δ 0.3 12 0.06 9 0.06 14 11 CTL OLP CTL OLP

Figure 15. mRNA levels of PTGS2, NAPEPLD, FAAH and NAAA in oral biopsies from OLP patients and healthy controls. Scatterplots representing N=13-14 from each cohort (controls are represented by grey circles, OLP patients by blue circles) including means represented by the bars. A change in ∆Ct values of -1 represents a doubling of mRNA. The left y-axis represents the experimental ∆Ct value and the right y-axis represents ∆Ct values relative to the expression of the control mean. The critical value of p was 0.0125 assuming a 5 % false discovery rate.

4.2 mRNA levels of NAPE, PTGS2, FAAH and NAAA in oral carcinoma cell lines SCC-25 and CAL 27 Preliminary experiments using oral carcinoma cell lines SCC-25 and CAL 27 treated with TNF-a and IL-8, key cytokines in the pathogenesis of OLP, showed no changes in FAAH, NAAA or NAPEPLD expression. However, an increase in mRNA levels of PTGS2 was seen with TNF-a (10 ng/mL) treatment. As previously, if comparing mRNA levels of PTGS2 relative to NAPEPLD rather than housekeeping gene RPL19, a median difference of -1.92 and -3.73 for the CAL 27 and SCC-25 cells, respectively, was found between TNF-a- and vehicle treated cells. This change corresponds to a roughly 4 and 13-fold change in expression of PTGS2 compared to NAPEPLD in cells treated with TNF-a. PEA, at concentrations of 0, 3 or 10 µM, were also included in the experiments (24 h incubation together with cytokines and vehicle) but no effects were seen (See Paper IV, Supplementary Figures S1 and S2).

NAPEPLD PTGS2 CAL27 SCC25 CAL27 SCC25 3 2

) 4 ) 5

RPL19 6 RPL19 7 Ct ( Ct ( ∆ ∆ 9 8

11 10 T T V V T T V V IL10 IL10 IL10 IL10 IL100 IL100 IL100 IL100 T+IL10 T+IL10 T+IL10 T+IL10 T+IL100 T+IL100 T+IL100 T+IL100

FAAH NAAA CAL27 SCC25 CAL27 SCC25 6 3

) 5

) 8

RPL19 7

RPL19 10 Ct ( Ct ( ∆ ∆ 12 9

14 11 T T V V T T V V IL10 IL10 IL10 IL10 IL100 IL100 IL100 IL100 T+IL10 T+IL10 T+IL10 T+IL10 T+IL100 T+IL100 T+IL100 T+IL100

Figure 16. Scatterplots representing preliminary experiments (N=3-4) with CAL 27 and SCC-25 cells (2,5 x 105 cells per well) seeded in 24-well plates were treated for 24h with vehicle (V; DMSO, final concentration 0.3 %), recombinant human TNF-α (10 ng/mL final concentration), IL-8 (10 or 100 ng/mL final concentrations).

4.3 NAE and PG levels in biopsies from OLP patients and healthy controls In a separate cohort, data from OLP patients and controls (N = 8 per group) were analysed for NAEs and PGs using UPLC-MS/MS. As the absolute amount of tissue was unknown for the embedded tissue samples, our approach was to calculate the data for each NAE as a proportion of the total NAEs found in each sample. We found that the most abundant NAEs in both groups were PEA and SEA (representing 92-97 % of total NAE recovered). However recovered levels of OEA only represented 0.53 % of the total NAEs recovered. Table 5 shows the relative abundance of NAEs found.

Table 5. Relative abundances of NAEs as % of total NAEs (in descending order of abundancy in the controls) in the OLP and control biopsy samples

Control (N=8) OLP (N=8)

NAE (%) Median Range Median Range

SEA 54.4 49.2 to 60.1 56.9 51.9 to 76.1

PEA 41.8 36.4 to 46.1 38.7 18.1 to 44.2

POEA 1.04 0.88 to 1.27 1.19 0.73 to 1.50

AEA 0.79 0.70 to 0.94 0.75 0.32 to 0.83

LEA 0.47 0.30 to 0.57 0.47 0.22 to 0.98

OEA 0.41 0.39 to 1.01 1.25 0.52 to 4.57

DHEA 0.41 0.26 to 0.50 0.41 0.19 to 0.85

DEA 0.38 0.29 to 0.55 0.36 0.17 to 0.53

EPEA 0.14 0.09 to 0.21 0.13 0.05 to 0.19

Values represent the concentrations in the samples normalized to the sum of the nine NAEs detected. Abbreviations:), stearoylethanolamide (SEA), palmitoylethanolamide (PEA), palmitoleoyl ethanolamide (POEA), anandamide (AEA), linoleoyl ethanolamide (LEA), oleoylethanolamide (OEA), docosahexaenoyl ethanolamide (DHEA), docosa tetraenoyl ethanolamide (DEA), eicosapentaenoyl ethanolamide (EPEA)

The same samples were also investigated for prostaglandin levels, normalized to the total levels of arachidonic acid based oxylipins, and the data is presented in Table 6. As the data presented in Tables 5 and 6 are compositional, it requires transformation prior to any statistical analysis (Aitchison 1982). We used the additive log ratio (alr) transformation where the log2 (parameter of interest / reference parameter) is calculated (Aitchison 1982) in order to determine alrs for PGs relative to PEA (analogous to the log2(PTGS2/NAPEPLD values described in the previous sections). This was not possible for individual PGs given the presence of zeros, and so the three PGs were added to give a total amount. Thus, for example, in one of the control samples, PGD2, PGE2 and PGF2a levels recovered were 20.29 and 25 pmol, respectively, giving a total PG amount in the sample of 74 pg. The PEA recovered was 1830 pg, so the alr for that sample is log2(74/1830) = -4.63. Using this method, the mean log2(tot PGs/PEA) were controls, -2.79; OLP, +1.98, i.e. a difference of 4.77 (95 % confidence interval 1.61 - 7.94) p = 0.0071, Welch’s t-test. The mean value of 4.77 translates to a 27-fold increase of PGs in OLP patients compared to controls, although the confidence interval is wide the sample size is relatively small.

Table 6. Prostaglandin levels in OLP and control biopsy samples as additive log ratios (alr) with PEA as denominator

Control (N=8) OLP (N=8)

Median Range Median Range

PGD2 1.44 0 to 8.90 1.70 0 to 4.97

PGE2 1.31 0 to 6.76 6.65 1.52 to 16.03

PGF2α 0.92 0 to 8.48 1.87 0.52 to 11.43

Values are normalized to arachidonic acid based oxylipins and analysed as log2(tot PGs/PEA) Abbreviations: Prostaglandin D2 (PGD2), Prostaglandin E2 (PGE2), Prostaglandin F2a (PGF2a).

Discussion

The aim of this present thesis was to address some of the knowledge gaps regarding chronic pain described in the introduction. The formal aims are reiterated here and discussed in turn:

1. To investigate differences in Swedish and Australian medical student attitudes and beliefs about chronic pain, its management, and the way it is taught (Paper I)

2. To determine by use of a best-worst scaling experiment with final year medical students and general practitioners which factors influence chronic pain management (Paper II)

Our main findings from Papers I and II were that Swedish final year medical students have a more positive attitude towards chronic pain patients compared to Australian students; overall both student cohorts are knowledgeable regarding key terms in chronic pain management but they request more chronic pain education in their curricula. Preferences for chronic pain management were explored utilizing a best-worst experiment with the final year medical students and also Swedish GPs. We found that Swedish medical students and GPs prioritised similar factors when choosing treatment for a new chronic pain patient in primary care, and that there were significant differences between factors prioritized by Australian students.

Attitudes and beliefs about chronic pain patients and views on chronic pain education by final year medical students in Sweden and Australia In our survey, we found that Swedish medical students had a more positive attitude towards chronic pain patients in regard to functional impairment, compared to their Australian counterparts. The observed differences were suggested to be due to cultural differences, differences in national or regional guidelines or differences in medical curriculum. We further found that the medical students called for more chronic pain education as they found it to be lacking, especially seeing how commonly patients suffer from chronic pain. The aspects frequently mentioned by our students were: seeing more patients, pharmacology including treatment plans and expectations, non-pharmacological treatments and more focus on the biopsychosocial model including learning together with other health professions in interdisciplinary teams.

HC-PAIRS scores can be problematic to compare between studies as there are differences in both the use of Likert scales and HC-PAIRS items. However, we made an attempt at this by normalizing HC-PAIRS scores from other studies assessing attitudes on different types of health care students in Paper I (See Paper I, Table 4). Our cohorts’ scores could be placed somewhere in the mid-range of the reported literature, and hence they are suitable to be compared with each other and the results of other studies. Rating scales can be skewed so that in theory it may be either too easy to obtain a good score (ceiling effect) or the opposite, too hard to obtain a good score (floor effect) (Šimkovic and Träuble 2019).

Physiotherapists and physiotherapy students often have more helpful beliefs (i.e. lower HC-PAIRS score) about chronic pain (Latimer et al. 2004, Overmeer et al. 2009, Briggs et al. 2013), and they have also shown to be more in line with clinical guidelines than students of other health care professions (Briggs et al. 2013). A questionnaire-based study in the UK found that physiotherapy students had better knowledge of chronic pain than medical students even though medical students scored higher in chronic pain management (Ali and Thomson 2009). But even with physiotherapists, education has shown to improve HC-PAIRS scores: a controlled study by Colleary et al. (2017) showed that physiotherapy students undergoing a 70-minute educational intervention in pain neurophysiology improved their knowledge and HC-PAIRS (a reduction by 18 points on a scale ranging from 13-91) scores significantly compared to students who followed the regular curriculum. They also made better treatment recommendations (Colleary et al. 2017). In the study by Briggs and colleagues comparing different health care practitioners’ attitudes and beliefs of chronic lower back pain, pharmacists had the least helpful beliefs of all (Briggs et al. 2013). The fact that physicians and pharmacists seem to have less beneficial beliefs about chronic pain can perhaps be explained by the tools of the trade. Physiotherapists have confidence in the ability of restoring function and movement to their patients’ bodies. Pharmacists, on the other hand, are equipped mainly with biomedical approaches such as drug regimes, which in chronic pain is not always an easy tool to use. The same may be true for many physicians. However learning from physiotherapists, and other health care professions in general in regards to chronic pain management, was something that the students in our cohort deemed important. Interactive learning with students from various professions have shown to improve communication skills and also motivate good future interprofessional relationships (Pollard and Miers 2008).

Pharmacology was another important topic from our results. A US study aiming to evaluate the undergraduate medical school curricula using Knowledge and Attitudes Survey Regarding Pain (KASRP), found that more than half of the medical residents gave the wrong answer to several questions regarding opioids

(Garcia et al. 2019). It is clear that pharmacological knowledge is an important skill, that is part of the curriculum in varying degrees. In the university of our Swedish student cohort, basic pharmacology is taught as a 5-week course at semester 5 (year 3), whilst for the Australian students in our study, pharmacology is not taught as a discrete subject and is instead given as lectures throughout the courses. This difference was reflected in our results as we found Australian students more often calling for more basic pharmacology lectures, and Swedish students aimed their goal at being able to utilize their knowledge in the clinic by asking to implement and gain further understanding of current treatment plans and guidelines.

Overall, the Australian and Swedish students called for more education on chronic pain. Studies have shown that allowing time for more education on chronic pain has a positive impact on chronic pain knowledge, indeed even a one- day workshop on pain management with various health care students can improve knowledge on chronic pain (Hadjistavropoulos et al. 2015). Moreover, pain management techniques learned by medical students during undergraduate training remains with them long afterwards (Stevens et al. 2009). In Canada, a pain curriculum based on the IASP-curricula was implemented in a range of health care undergraduate programmes, and was well-received (Watt-Watson et al. 2004). A pain curricula can either be integrated as spanning over the whole undergraduate education, or as a cohesive course during some weeks. Different approaches might fit better for different universities (Shipton et al. 2018).

Finally, the majority of chronic pain patients are managed in primary care (Breivik et al. 2006) and even though there are specialists managing chronic pain within the specialities of neurology, anaesthesia and rehabilitation medicine, there is no need or capacity for all chronic pain patients to be managed in specialist care. It is thus important that medical school and general practitioners are sufficiently aware of the best management practices (Loeser 2015). Improving the current status of medical undergraduate education will require actions at local and national levels to improve chronic pain management. Furthermore, clinical culture (the “hidden curriculum”) needs to be addressed to improve attitudes and behaviour towards chronic pain patients (Ellis et al. 2012).

Factors imacting the treatment choices of final year medical students and general practitioners Although 40 years have elapsed since the biopsychosocial model was first described, the biomedical model for understanding chronic pain is still the prime focus of many HCPs for the management of chronic pain, even though it has been linked to negative association with patient education, adherence to treatment guidelines, reported work and activity recommendations (Darlow et al. 2012).

The choices made as ”most important” by necessity mean that other choices will have a lower priority, but this will have consequences for treatment. The discussion below therefore concentrates on the factors that were not chosen, or chosen as least important. As a caveat it should be kept in mind that this survey uses a relatively uncomplicated hypothetical pain vignette, and when forced to choose between factors, the ones not chosen or chosen as least important are not necessarily ignored by physicians and medical students in real situations, it just shows that when forced to choose, this is how they prioritize. Nontheless, it is concerning that important non-pain assessment factors, such as social support, patient preferences and treatment adherence were ignored or even chosen as ‘least important’ by our students and GPs.

The factors chosen as most important by all three cohorts in our study were typical pain assessment factors such as patients’ pain description, average pain rating over the past week, previous treatment experiences and treatment history. Although Sweden and Australia have comparable approaches to chronic pain management, we did find differences between Swedish and Australian cohorts. The factor ‘your professional experience’ differed significantly between all groups, where the Swedish GPs considered it one of their most important factors. This result is in line with previous studies (Hollingshead et al. 2015). However, Swedish medical students chose this factor as their most important more often than the Australian students, who indeed had this factor amongst their bottom three choices. We hypothesise that one reason for this could be that Swedish medical students might encounter more patients during their training.

Regarding pain rating factors, we found that Swedish medical students favoured ‘patients’ current pain rating’ compared with Australian students who more often chose ‘patients’ pain rating over the past week’. The GPs did not tend to give attention to the pain rating factors and were thus in-line with previous literature, showing that physicians tend to not consider pain ratings that frequently (Bijur et al. 2006, DeRuddere et al. 2013).

Patients’ voice and facial expression when describing their pain was a factor often chosen as least important by all three cohorts. There is evidence that self-reported pain and pain behaviour measurements (such as facial expressions) are not consistent, especially for chronic pain patients (Labus et al. 2003). However, face scales in chronic pain management can occasionally be useful, in particular when there are limited lines of communication with the patient such as children or cognitively impaired patients (which was not the case of our vignette). We suggest that to some extent, participants can consider this factor to be covered in ‘patients’ pain description’. On the other hand, the factor ‘patient preferences’, also one of the factors most often chosen as ‘least important’ by the Swedish GPs, is a clearly separate factor from patients ‘pain description’. As previously

discussed in the introduction of this thesis, although patients’ preferences are important, doctors may not necessarily take this into consideration (Pedersen et al. 2012). Our results are in agreement with this conclusion. Involving patients and their preferences in treatment decisions are associated with enhanced treatment satisfaction, completion, and clinical outcomes (Lindhiem et al. 2014). Swedish and Australian medical students apply patient centred care in their curriculum, however, this factor was also neglected by the students.

Social support was considered one of the least important factors by the Swedish students and GPs. Social support has long been suggested to have an impact on not only the general psychosocial wellbeing and pain, but also the adherence to medical interventions and medications (Gottlieb 1985). A high level of social support is considered to be one of the protective factors for developing and maintaining chronic pain (Fillingim et al. 2014). Numerous studies have confirmed the relationship between social support and pain and disability (Kerns et al. 2002, Montoya et al. 2004, Wernicke et al. 2017, Landmark et al. 2018, Oraison and Kennedy 2019). Higher levels of social support have also been linked to better treatment adherence (Orainson and Kennedy 2019). Treatment adherence per se was a factor that was often not chosen by our cohorts. Medical non-adherence or -compliance, e.g. when the patient does not follow the recommended regimen for chronic pain medication is common. A systematic review by Timmerman et al. (2016) found a non-adherence rate ranging from 8- 62 %, where most common reasons for non-adherence was polypharmacy, pain intensity, concerns about the pain medication and dosing frequency.

Pharmacists, who are experts in the appropriate use of pharmacological interventions, have been suggested to be valuable members of interprofessional teams, but their skills are often overlooked and are regularly not part of this model (Kress et al. 2015). Several studies have proven the benefits of including pharmacists in interprofessional teams. A good example is a randomized controlled trial by Sjölander et al. (2019) showing that pharmacists working in hospital wards reduced the number of drug-related re-admissions and also reduced the cost per patient amongst older patients with dementia or cognitive impairment. Coffey et al. (2019) evaluated the impact of a pharmacist held chronic pain class and a medication therapy management visit for chronic pain patients, and showed that patients reported decreased pain scores (baseline score 8.3 decreased to 5.6 out of 10 on a numeric rating scale), a 40 % decrease in opioid doses, an 80-92 % approval rate of the intervention and a general increase in patient satisfaction with care. The British Pain Society describe the role of the pharmacist in interprofessional chronic pain management teams to be the “education and planning of medication adjustments” (British Pain Society 2013).

A final consideration concerns the demographics of the patients. All three cohorts in our survey assigned patients’ demographic as part of their least important factors, a result in keeping with the study of Hollingshead et al. (2015). Although using demographic factors as basis for decisions in chronic pain management is not clinically motivated, the knowledge of bias in regards to demographic factors such as gender, age and ethnicity towards chronic pain patients is important (Green et al. 2003, Stålnacke et al. 2015). In a review of the available data by Samulowitz et al. (2018), it was found that the gendered norms towards chronic pain patients were that women in pain are described as emotional, more sensitive and even sometimes hysterical, while men were described as stoic and not seeking healthcare. The opposite was found by Rovner et al. (2017) studying sex differences in pain behaviour where women had higher acceptance, activity level and social support and men higher fear of movement and mood disturbances.

The finding that Swedish medical students and GPs have very similar choices further underline the importance of improving the medical curriculum in order to equip the medical profession with the correct tools to treat chronic pain. Implementing new guidelines in active professionals are not always successful (Grol and Grimshaw 2003). Education on an undergraduate level can thus be an important pathway to achieving better adherence to updated clinical guidelines, improving attitudes and thereby contribute to a more optimal management of chronic pain.

The two final aims of this thesis were:

1. To investigate if PEA has direct effects upon COX-2 (Paper III)

2. To investigate the levels of PEA, its synthesising, and hydrolysing enzymes in OLP-patients relative to the levels of COX-2 and its prostaglandin products (Manuscript IV)

The results from these aims will now be discussed in turn.

Effects of PEA on COX2 expression and activity in RAW 264.7 cells In our experiments we found, as expected from the literature, that 24 h treatment with LPS + IFN-g significantly induced COX-2 upregulation on both mRNA and protein levels. We found no significant effects of PEA on mRNA levels of Ptgs2, Faah or Naah. However, we did find that genes coding for PEA hydrolysing enzymes, Naaa and Faah, change their relative balance in favour of Faah after 24 h treatment with LPS + IFN-g in RAW 264.7 cells.

The lack of effect of PEA upon Ptgs2 and COX-2 expression differ from other published in vivo data: Costa et al. (2002) found that PEA, to a comparable extent to the NSAID indomethacin, reduced COX-2 activity (measured in paw tissue ex vivo), nitric oxide, free radicals, and carrageenan induced oedema in a rat model of inflammation. D’Agostino et al. (2009), found that PEA inhibited NF-kB and mediated inflammation in mouse model through PPAR-a. We utilized a cell model of inflammation and our experiments were thus not performed in similar conditions as the above mentioned in vivo studies. Even though macrophages are commonly used for studying PEA, to our knowledge, studies of the impact of PEA in RAW 264.7 cells besides our Paper III are scarce; Ross et al. (2000) reported that 10 µmol/L PEA reduced nitric oxide (NO)-production by ~ 40 % in LPS- stimulated RAW 264.7 cells.

TLR-mediated activation of macrophages through LPS induces NF-kB translocation resulting in translation and release of cytokines such as IL-6 and TNF-a (Liu et al. 2017). An upregulation of genes coding for TNF-a was seen during our experiments with J774.2 cells. We further saw a decrease in Napepld expression that was in agreement with results by Rinne et al. (2018) who also found decreased expression of genes coding for this enzyme in activated mouse primary macrophages. Other studies have shown that PEA reduces the expression

of COX-2, iNOS and hence the release of cytokines such as TNF-a (Benito et al. 2012). We did however not see any effects of PEA on cytokine regulation at the mRNA level in J774.2 cells.

There are a few studies reporting altered levels of FAAH and NAAA. In a model of ulcerative colitis, NAAA was found to be decreased whilst FAAH was increased (Suárez et al. 2012) and in patients with chronic lower back pain, FAAH has been reported to be increased (Ramesh et al. 2017). These results are in line with what we found in RAW 264.7 cells. Furthermore, our results indicate that inhibition of both FAAH and NAAA is required to diminish hydrolysis of PEA. This is important to consider in development of treatments utilizing enzyme inhibition to increase endogenous levels of PEA. In animal models, both FAAH and NAAA inhibitors reduce carrageenan-induced paw oedema in mice (Holt et al. 2005, Sasso et al. 2018), but to our knowledge, there has been no overt drug discovery strategy with respect to combined FAAH / NAAA inhibitors. However, a clinically used chemotherapeutic 5-fluorouracil-releasing agent, Carmofur, has been discovered to work as a dual FAAH and NAAA inhibitor, inhibiting the enzymes in the low micromolar range, reducing mRNA levels of genes coding for IL-1b, IL- 6, iNOS and TNF-a and NF-kB signalling in LPS-treated RAW 264.7 cells in a manner sensitive to the PPAR-a inhibitor MK886, increasing levels of PEA (as well as AEA and OEA) in the lungs of mice and attenuating LPS-induced lung injury in mice (Wu et al. 2019).

Previous studies in the laboratory have demonstrated that LPS + IFN-g treatment of RAW 264.7 cells produce increases in 11- and 15- (but not 12-) HETE levels in addition to the increases in PGD2 and PGE2 (Gouveia-Figueira et al. 2015). COX- 2 has been argued to be an important source of 11- and 15-HETE in macrophages, in a manner that was aligned with COX-2 activity rather than lipoxygenases that is otherwise considered to be their main synthesising enzymes (Norris et al. 2011). Furthermore, Gouveia-Figueira et al. (2015) found that COX-2 inhibitors also inhibited production of 11- and 15-HETE in activated RAW 264.7 macrophages.

Our data showing a) that levels of Alox15, coding for the 15-HETE synthetic enzyme LOX-15, are low in the RAW 264.7 cells and are not overtly increased by LPS + IFN-g treatment (Paper III, Supplementary Figure S2), and b) that PEA during a 24 h incubation with LPS + IFN-g, significantly reduced the levels of 11- and 15-HETE by a similar extent to that seen for PGD2 and PGE2, support the hypothesis that COX-2 activity is the main source of 11- and 15-HETE in the RAW 264.7 cells. These results are in agreement with other studies showing decreased levels of 15-HETE following PEA-treatment: Lerner et al. (2019) found that prophylactic treatment with PEA in mice reduced 15(S)-HETE in spleen, mRNA

levels of Il1b in spleen and levels of Ptgs2 in spleen and hippocampus. Furthermore, increased levels of 15-HETE have been found in patients with knee osteoarthritis (Attur et al. 2015). Native COX-2 can produce both 15(R)- and 15(S)-HETE (Mulugeta et al. 2010).

Direct effetcs of PEA on COX-2 enzymatic activity Regarding the direct effects of PEA on the COX-2 enzyme, we used an Oxygraph system that can directly measure the oxygen consumption that takes place during the conversion of AA (or 2-AG) to PG by COX-2. COX-2 inhibition has been suggested for the long-chain fatty acid endocannabinoid N- docosahexaenoylethanolamine (DHEA) by Meijerink et al. (2015) as they found that DHEA decreased metabolites from AA-conversion of COX-2 (PGD2, PGE2, PGF2α, TXB2, 8-iso-PGF2α and 12-HHTrE) and IL-6 in RAW 264.7 cells. In addition, they reported decreased NO-release, but no impact on the upstream transcription factor, NF-kB. The opposite effects upon PGE2 were seen with the corresponding docosahexaenoic acid. Hence, we evaluated the probability of PEA having similar action, through direct effects upon the COX-2 enzymatic activity. We did however not find any such effects with PEA. After Paper III was published, similar effects have been reported with other n-3 long-chain polyunsaturated fatty acid derivates in RAW 267.4 cells; N-docosahexaenoyl dopamine (Wang et al. 2016) and N-eicosapentaenoyl dopamine (Augimeri et al. 2019). In contrast to the studies with DHEA and the two dopamine derivates, we saw no overt influence of PEA upon the cycloxygenation of AA and 2-AG by COX-2. This difference presumably reflects the structural difference between DHEA and PEA: although they are both fatty acid amides, the docosahexaenoic acid side chain is unsaturated while palmitoyl side chain is saturated. DHEA has structural similarities with AEA, which is metabolised by COX-2 (Kozak et al. 2001, 2002). In respect, palmitic acid (100 µM) is not a substrate for COX-2 but can bind to the enzyme and produces a 28 % increase in the rate of cycloxygenation of 20 µM AA by human recombinant COX-2 in an oxygen electrode (Yuan et al. 2009).

Thus, it can be concluded that in RAW 264.7 cells: a) levels of Naaa are greater than Faah, although both enzymes are involved in the hydrolysis of exogenous PEA and b) PEA reduces the production of oxylipins PGD2, PGE2, and 11- and 15- HETE in response to LPS + IFN-g treatment though a mechanism that is not related to direct effects upon the catalytic activity of COX-2. Further experiments are needed to elucidate the mechanism(s) involved.

Increased levels of PTGS2 and possibly NAPEPLD in OLP patients During our investigations of OLP tissue biopsies by qPCR we found that PTGS2 mRNA levels were increased approximately fourfold compared to controls. As increases in COX-2, both at the mRNA and protein level, are one of the hallmarks of OLP, this was an expected result. Moreover, Chankong et al. (2016) performed immunohistochemistry on epithelia from OLP patients reporting a 1.4-fold increase for COX-2 staining in OLP patients compared to controls, and Abdel Hay et al. (2012) found an increase in tissue COX-2 expression to be related to an increase in tissue PGE2. COX-1 is also a producer of PGs, yet it has been found that in OLP, COX-2 expression is increased by 2-fold compared to COX-1 (Lysitsa et al. 2008).

We found no changes in NAE hydrolysing enzymes NAAA and FAAH in OLP patients, indicating there is no increased degradation of NAEs in OLP, at least in our patient cohort. We found the NAE synthesising enzyme NAPEPLD to be increased, however the p-value was above the limit of the critical value of p using a 5 % false discovery rate. It can be discussed whether the use of corrections for multiple testing is required for exploratory data as much as for confirmatory data (Rothman 1990), but our way of giving unadjusted p values together with the critical value of p allow the reader to reach their own conclusions as to the potential importance of our findings. Levels of NAPE-PLD and other NAE-related enzymes have not been studied in OLP before, hence comparisons to our data is unattainable. To our knowledge, the only published study of NAPEPLD in oral tissues was that of Alhouayek et al. (2019b), who found increased expression of NAPEPLD in tumours from patients with oral tongue carcinoma. Further investigations are needed into NAPE-PLD expression, in particular at the protein level, in OLP.

Additionally, we analysed the data in a manner relevant to the PGs and PEA levels, in which we compared PTGS2 to NAPEPLD instead of the reference gene RPL19. By this approach, it could be concluded that the increase in PTGS2 corresponded to about a doubling in expression relative to NAPEPLD in OLP patients compared to controls.

Effects of TNF-a upon the expression of PTGS2 in oral carcinoma cells Amongst the cytokines involved in OLP pathogenesis TNF-a and IL-8 are important players for the development and/or progression of the disease. In the results from these preliminary experiments using oral carcinoma cells, we can conclude that stimulation with TNF-a in SCC-25 and CAL 27 oral carcinoma cell

lines leads to an increase of PTGS2 mRNA levels. IL-8 per se did not contribute to this effect. Investigating this data in the same manner as for PGs and NAEs in oral biopsies (see below) resulted in a median difference in mRNA expression between vehicle- and TNF-a treated cells of -3.73 and -1.92 for SCC-25 and CAL 27 respectively. This corresponds to a 13-fold and a fourfold expression of PTGS2 relative to NAPEPLD in TNF-a-treated cells compared to vehicle.

Previous studies from our laboratory have presented a 4-fold increase in mRNA levels for PTGS2 in TNF-a treatment of DU145 human prostate cancer cells including producing a small decrease in NAPEPLD and no significant changes in FAAH and NAAA expression (Karlsson et al. 2017). Although the present findings indicate that treatment of the cells with a known pathogenic factor mimic the sort of changes in PTGS2 relative to NAPEPLD seen in OLP, further research is needed to conclude whether SCC-25 and/or CAL 27 commercial cell lines are suitable for as a model of studying possible treatment effects for OLP. PEA was also investigated in these cell studies, but was not found to show any obvious effects upon PTGS2 expression under the conditions used, a result consistent with the data using a macrophage cell line (Paper III).

NAE and PG levels in biopsies from OLP patients The two main findings from the second cohort were 1) the low levels of oleoylethanolamide (OEA) relative to PEA and stearoylethanolamide (SEA) in the tissues, and 2) the increased PG levels relative to PEA in OLP. In many tissues/fluids, OEA is a major NAE, but only accounted for ~0.5% of the NAEs in our biopsy samples. According to the literature, OEA is usually present in the same amounts as SEA and PEA in human plasma (Barry et al. 2018, Gachet et al. 2015). As NAE levels have not been studied in OLP biopsies before, one explanation for the observed results could be a low abundance of oleoyl-species in precursor lipids. However, in tongue biopsies, the relative abundance of OEA was not found to be deviant from levels in plasma (Halczy-Kowalik et al. 2019).

As mentioned in the introduction, some studies of PEA and other NAEs report increased tissue levels, such as acute stroke, chronic neck pain, post-traumatic stress disorder and chronic migraine (Darmani et al. 2005). Increased levels of SEA and PEA were found in the trapezius muscle of women with chronic widespread pain and chronic neck shoulder pain and the levels were correlated with pain intensity and sensitivity (Ghafouri et al. 2013). Given the anti- inflammatory properties of PEA, it could be argued that changed PEA levels in a given disorder could be interpreted as an endogenous response to inflammation (in the case of an increase), or a factor for prolonging chronic inflammation (in the case of a decrease). Due to the nature of our samples, we were not able to determine whether PEA and/or other NAEs were affected in OLP, but we could

note that the relative proportions of the major NAEs were not significantly changed. Given that the NAEs share the same synthetic route, this is not a surprising finding.

When we investigated prostaglandin levels in OLP biopsies (the same cohort as NAEs) we found a 27-fold increase of PGs relative to PEA in OLP patients compared to controls, albeit with a wide confidence interval. These results are in accordance with the observed imbalance between PTGS2 and NAPEPLD seen in the other OLP cohort above. Generally when analysing qPCR data it should be kept in mind that an increase in mRNA levels does not necessarily result in a translation into functional proteins. However, we did see an increase in COX-2 derived PGs in our other cohort of OLP patients which confirms the observed upregulation of PTGS2. The imbalance between PGs and PEA opens up the possibility that supplementation of PEA to the patients, in the form of a locally acting cream, might be a useful strategy for the treatment of OLP.

Future perspectives

In conclusion, the results presented in this thesis indicate that:

I) Australian and Swedish final year medicals students are knowledgeable in key concepts of chronic pain management and have similar attitudes and beliefs as other health care students from the literature, but they request more education on chronic pain.

II) Swedish medical students and Swedish GPs prioritize similar factors compared to Australian medical students when choosing treatment for a novel chronic pain patient in a primary care setting. These results underline the importance of appropriate chronic pain education during medical training.

III) Treating mouse macrophage cell lines with PEA reduces PG levels through a mechanism that is not occurring via direct effects upon COX-2.

IV) The balance between PGs and PEA is altered in patients with OLP (where the shift in relative proportion between PGs and PEA is towards PGs). Local PEA treatment could thus prove to be a useful treatment strategy for OLP.

Future research in this field should address the translation of chronic pain education into practice; how improved education improves attitudes and beliefs of HCPs, their guideline practice, communication skills and subsequent patient outcomes. Furthermore, investigations into the capacity to integrate a national chronic pain curriculum, preferably using the IASP curriculum as template, should be undertaken, preferably a model where where the medical students are taught together with students in other health care professions. Since a multimodal view of chronic pain requires collaborating across different health care professions, the inclusion of additional professions, such as pharmacists should be considered. Focus should also be upon what is taught in informal training (clinic) in terms of attitudes e.g. “the hidden curriculum” and how to impact this area of education.

Regarding PEA and its mechanism of action, it is of value to gain understanding by which mechanism(s) PEA reduces PG levels without directly affecting COX-2 (or COX-1) expression or enzyme kinetic properties. Considering its potential as a novel treatment for chronic pain, there is a need for further investigations into endogenous tissue levels of PEA (other NAEs and related enzymes in patients with pain or inflammatory diseases of different aetiologies) in order to investigate if there is a clear pattern in PEA-levels decreasing or increasing depending on the condition. In general, further investigations are warranted into the

pharmacokinetic properties of PEA as well as an overall improvement regarding clinical trials of PEA in treatment of chronic pain in order to assess its benefits.

Finally, oral lichen planus is a common disease that is in need of novel treatment. Our findings suggest that there is an imbalance between PGs and PEA in OLP, and that, at the mRNA level of the synthetic enzymes, can be mimicked by treatment of commercially available oral carcinoma cells with TNF-a. It would clearly be of interest to determine if this simple model can mimic other biochemical changes seen in OLP, and whether PEA can reverse such changes. Ultimately, the hope is that the potential of PEA as a treatment for OLP could be investigated in clinical trials.

Acknowledgements

This has been an exciting journey that I never saw for myself. I started as a student fresh from the pharmacy master programme and got the privilege to continue learning and develop throughout this PhD, not only in academia but also in my personal life, as I met my husband, got married, we had our first child and I got my driving license (I’m a proper adult now!). Needless to say throughout this time I have experienced much more than I thought life would give.

But as proud as I am of my personal achievement and this thesis, this research is the result of many collaborations, mentorship and teamwork. The time has come to thank everyone that somehow contributed to this thesis.

Christopher Fowler – How can I express how utterly grateful I am to have had you as my supervisor? You never cease to amaze me and you have shown that throughout a long scientific career, constant development and learning of new skills is making the job exciting. You have equipped me with all the tools for being a good scientist. However, I will never understand numbers (or some of your jokes) the way you do but I will try my best to continue your tradition in publishing and using appropriate statistics. You are a true inspiration and have been a great support throughout this time. Thank you.

Gisselle Gallego – When I started this PhD I never could have thought I would end up combining mixed methods survey research with lab work, on top of travelling across the world to beautiful Australia to work with you. Your social and networking skills are impressive and important in any career. Thank you for giving me the opportunity to try different things and to explore my own preferences of different fields in research.

Sofia Mattsson – You were one of my favorite teachers during my own studies and it has been fantastic to teach together with you on your courses. You have such an effortless easygoing attitude combined with great skills that I truly admire. Thank you for showing me that teaching lab to a whole class of students is quite fun and to not always take things too seriously.

Stig Jacobsson – Even though you have not been directly involved in my research, I feel you have been mentoring me regarding my general career and situation at the department throughout all this time, especially during the long “utvecklingssamtal” where I could vent and discuss the worksituation with you. Your teaching skills are an inspiration, but mostly I want to thank you for giving me the opportunity to start a PhD at the department in the first place.

Former PhD student Jessica Karlsson – Even though you were quite hesitant in my approaches to befriend you at first, you finally gave in after a few years. It has been great being able to work alongside you and learn from your lab skills. You were always a joy to work with and learn from. I also really enjoyed the scientific discussions in our office, it has been great to have the non-prestigious exchange of thoughts and ideas. Not to mention all the support during hard times we have been able to give each other throughout. Thank you for being a great colleague and friend.

Former post doc (and maid of honor) Sandra Gouveia-Figueira. You have such a great personality, it is impossible not to like you! Obrigado por ser um amigo solidário. You know you are very special to me as you were one of the first friends I made outside of work being relatively new in Umeå. Thank you for being so good at keeping in touch and also for teaching me how to make sushi. Now we get to enjoy seeing our sons grow up together.

Former PhD students Dina and Emmelie, and post-docs Mireille and Sanaz. Even though I knew you would all leave the department before me, I can’t help but feeling a little bit lonely thinking back to how many we used to be working at the (previous) department. You have all enriched me with your expertise, friendship, and culture (well, in Emmelies case it’s a form of patriotism for مﺮﮑﺸﺘﻣ and ﺮﻜﺷ ا ﺰﺟ ﯾ ﻼ ,Boden) and I’m very grateful for that. So благодарю вас, tack to all of you!

Agneta Valleskog you have a great personality, you challenge the dry scientists of any workplace with culture, feminism and Norrländsk vemods-humour. Thank you for inviting me to cultural events, your home and your family.

Nadia, Olov, Mona, Martin, Gunnar, Ulrika, Helena, Maria G, Maria S and Sofia S, thank you for being great co-workers, inspiring teachers and scientists. Thank you for all the laughter in the lunchroom and for putting up with my politics and Norrbotten-humour. A special thank you to the pharmacy teachers who all do such inspiring and diverse things as pharmacists. You make me proud to be a pharmacist amongst all the biologists, chemists, physicians and bio-medicine-people.

Greg, thank you for being my perfect match in life and for being an amazing father to our lovely Alexander (and the upcoming new member of our little family). Nothing feels too difficult with your love and support. Thank you also for being such an inspiring, hardworking and humble scientist. We do make a great team and I love you with all my heart.

My family, mamma, pappa, bonus-parents, grandparents, brother and step- siblings. Thank you for your support and for sounding impressed and pretending to understand when I try to explain what I do for a living. Perhaps it became more clear today for those of you who witnessed me present this work. Hopefully we can have a party and celebrate this when the time is right.

To all of my friends, thank you for being you, for all the pub-nights, quizzes, dinners, board-games, picknicks, movie-nights, long walks and chats. I’m so lucky to have you and I’m glad so many of you are parents, scientists and former PhD students that I have been able to vent and complain to during the rough times and celebrate the good times.

Yours sincerely,

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96 Appendix

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