Molecular mechanism of α-synuclein aggregation on lipid membranes revealed.

The central hallmark of Parkinson's disease pathology is the aggregation of the α-synuclein protein, which, in its healthy form, is associated with lipid membranes. Purified monomeric α-synuclein is relatively stable in vitro, but its aggregation can be triggered by the presence of lipid vesicles. Despite this central importance of lipids in the context of α-synuclein aggregation, their detailed mechanistic role in this process has not been established to date. Here, we use chemical kinetics to develop a mechanistic model that is able to globally describe the aggregation behaviour of α-synuclein in the presence of DMPS lipid vesicles, across a range of lipid and protein concentrations. Through the application of our kinetic model to experimental data, we find that the reaction is a co-aggregation process involving both protein and lipids and that lipids promote aggregation as much by enabling fibril elongation as by enabling their initial formation. Moreover, we find that the primary nucleation of lipid-protein co-aggregates takes place not on the surface of lipid vesicles in bulk solution but at the air-water and/or plate interfaces, where lipids and proteins are likely adsorbed. Our model forms the basis for mechanistic insights, also in other lipid-protein co-aggregation systems, which will be crucial in the rational design of drugs that inhibit aggregate formation and act at the key points in the α-synuclein aggregation cascade.

Behavioral Science: Enhancing Our Approach to the Development of Effective Additional Risk Minimization Strategies.

Additional risk minimization strategies may be required to assure a positive benefit-risk balance for some therapeutic products associated with serious adverse drug reactions/risks of use, without which these products may be otherwise unavailable to patients. The goals of risk minimization strategies are often fundamentally to influence the behavior of healthcare professionals (HCPs) and/or patients and can include appropriate patient selection, provision of education and counselling, appropriate medication use, adverse drug reaction monitoring, and adoption of other elements to assure safe use, such as pregnancy prevention. Current approaches to additional risk minimization strategy development rely heavily on information provision, without full consideration of the contextual factors and multi-level influences on patient and HCP behaviors that impact adoption and long-term adherence to these interventions. Application of evidence-based behavioral science methods are urgently needed to improve the quality and effectiveness of these strategies. Evidence from the fields of adherence, health promotion, and drug utilization research underscores the value and necessity for using established behavioral science frameworks and methods if we are to achieve clinical safety goals for patients. The current paper aims to enhance additional risk minimization strategy development and effectiveness by considering how a behavioral science approach can be applied, drawing from evidence in understanding of engagement with pharmaceutical medicines as well as wider public health interventions for patients and HCPs.

Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning.

Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. To address this problem, we describe here a machine learning approach to identify small molecule inhibitors of α-synuclein aggregation, a process implicated in Parkinson's disease and other synucleinopathies. Because the proliferation of α-synuclein aggregates takes place through autocatalytic secondary nucleation, we aim to identify compounds that bind the catalytic sites on the surface of the aggregates. To achieve this goal, we use structure-based machine learning in an iterative manner to first identify and then progressively optimize secondary nucleation inhibitors. Our results demonstrate that this approach leads to the facile identification of compounds two orders of magnitude more potent than previously reported ones.

Health and illness beliefs in adults with tuberculosis infection during the COVID-19 pandemic in the UK.

Background: COVID-19 disrupted the TB prevention programme in the UK, especially for TB infection (TBI) care. We explore whether experience of the COVID-19 pandemic impacted on patients' perceptions of TBI and its treatment. Methods: Semi-structured interviews were conducted as part of the Research to Improve Detection and Treatment of TBI (RID-TB) programme, exploring perceptual and practical barriers to TBI treatment. Nineteen people diagnosed with TBI were interviewed between August 2020 and April 2021. Recordings were transcribed and analysed using a constant comparative approach, allowing for a dynamic and iterative exploration of themes. Themes are organised using the Perceptions and Practicalities Approach. Findings: Some participants perceived TBI as a risk factor for increased susceptibility to COVID-19, while some thought that treatment for TBI might protect against COVID-19 or mitigate its effects. Adaptations to TB services (e.g., remote follow-up) and integrated practices during the COVID-19 restrictions (e.g., medication being posted) addressed some practical barriers to TBI treatment. However, we identified beliefs about TBI and COVID-19 that are likely to act as barriers to engagement with TBI treatment, including: interpreting service delays as an indication of TBI not being serious enough for treatment and concerns about contracting COVID-19 in TB clinics. Interpretation: COVID-19 and TBI service delays influence people's perceptions and practical barriers to TBI treatment adherence. Failure to address these beliefs may lead to people's concerns about their treatment not being fully addressed. Utilised service adaptations like remote consultations to address practical barriers may be relevant beyond COVID-19. Funding: NIHR RID-TB Program (RP-PG-0217-20009).

Supporting people with type 2 diabetes in effective use of their medicine through mobile health technology integrated with clinical care (SuMMiT-D pilot): results of a feasibility randomised trial.

BACKGROUND: The purpose of this 6-month intervention pilot feasibility randomised trial was to test sending brief messages using mobile phones to promote self-management through taking medication as prescribed to people with type 2 diabetes. This was to inform the design and conduct of a future large-scale United Kingdom-based clinical trial and establish the feasibility of recruitment, the technology used, follow-up, and data collection. METHODS: A multicentre individually randomised, controlled parallel group trial in primary care, recruiting adults (≥ 35 years) with type 2 diabetes in England. Consenting participants were randomly allocated to receive short message system text messages up to four times a week, or usual care, for a period of 6 months; messages contained behavioural change techniques targeting medication use. The primary outcome was the rate of recruitment to randomisation of participants to the trial with a planned rate of 22 participants randomised per month. The study also aimed to establish the feasibility of follow-up at 6 months, with an aim of retaining more than 80% of participants. Data, including patient-reported measures, were collected at baseline and the end of the 6-month follow-up period, and a notes review was completed at 24 months. RESULTS: The trial took place between 26 November 2018 and 30 September 2019. In total 209 participants were randomly allocated to intervention (n = 103) or usual care (n = 106). The maximum rate of monthly recruitment to the trial was 60-80 participants per month. In total, 12,734 messages were sent to participants. Of these messages, 47 were identified as having failed to be sent by the service provider. Participants sent 2,864 messages to the automated messaging system. Baseline data from medical records were available for > 90% of participants with the exception of cholesterol (78.9%). At 6 months, a further HbA1c measurement was reported for 67% of participants. In total medical record data were available at 6 months for 207 (99.0%) of participants and completed self-report data were available for 177 (84.7%) of participants. CONCLUSION: The feasibility of a large-scale randomised evaluation of brief message intervention for people with type 2 diabetes appears to be high using this efficient design. Failure rate of sending messages is low, rapid recruitment was achieved among people with type 2 diabetes, clinical data is available on participants from routine medical records and self-report of economic measures was acceptable. TRIAL REGISTRATION: ISCTRN ISRCTN13404264. Registered on 10 October 2018.

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity.

In the early stages of drug development, large chemical libraries are typically screened to identify compounds of promising potency against the chosen targets. Often, however, the resulting hit compounds tend to have poor drug metabolism and pharmacokinetics (DMPK), with negative developability features that may be difficult to eliminate. Therefore, starting the drug discovery process with a "null library", compounds that have highly desirable DMPK properties but no potency against the chosen targets, could be advantageous. Here, we explore the opportunities offered by machine learning to realize this strategy in the case of the inhibition of α-synuclein aggregation, a process associated with Parkinson's disease. We apply MolDQN, a generative machine learning method, to build an inhibitory activity against α-synuclein aggregation into an initial inactive compound with good DMPK properties. Our results illustrate how generative modeling can be used to endow initially inert compounds with desirable developability properties.

UK clinicians' attitudes towards the application of molecular diagnostics to guide antibiotic use in ICU patients with pneumonias: a quantitative study.

BACKGROUND: Molecular diagnostic tests may improve antibiotic prescribing by enabling earlier tailoring of antimicrobial therapy. However, clinicians' trust and acceptance of these tests will determine their application in practice. OBJECTIVES: To examine ICU prescribers' views on the application of molecular diagnostics in patients with suspected hospital-acquired and ventilator-associated pneumonia (HAP/VAP). METHODS: Sixty-three ICU clinicians from five UK hospitals completed a cross-sectional questionnaire between May 2020 and July 2020 assessing attitudes towards using molecular diagnostics to inform initial agent choice and to help stop broad-spectrum antibiotics early. RESULTS: Attitudes towards using molecular diagnostics to inform initial treatment choices and to stop broad-spectrum antibiotics early were nuanced. Most (83%) were positive about molecular diagnostics, agreeing that using results to inform broad-spectrum antibiotic prescribing is good practice. However, many (58%) believed sick patients are often too unstable to risk stopping broad-spectrum antibiotics based on a negative result. CONCLUSIONS: Positive attitudes towards the application of molecular diagnostics to improve antibiotic stewardship were juxtapositioned against the perceived need to initiate and maintain broad-spectrum antibiotics to protect unstable patients.

Bayesian Optimization in the Latent Space of a Variational Autoencoder for the Generation of Selective FLT3 Inhibitors.

The process of drug design requires the initial identification of compounds that bind their targets with high affinity and selectivity. Advances in generative modeling of small molecules based on deep learning are offering novel opportunities for making this process faster and cheaper. Here, we propose an approach to achieve this goal, where predictions of binding affinity are used in conjunction with the Junction Tree Variational Autoencoder (JTVAE) whose latent space is used to facilitate the efficient exploration of the chemical space using a Bayesian optimization strategy. The exploration identifies small molecules predicted to have both high affinity and high selectivity by using an objective function that optimizes the binding to the target while penalizing the binding to off-targets. The framework is demonstrated for FMS-like tyrosine kinase 3 (FLT3) and shown to predict small molecules with predicted affinity and selectivity comparable to those of clinically approved drugs for this target.

Multiplexed Digital Characterization of Misfolded Protein Oligomers via Solid-State Nanopores.

Misfolded protein oligomers are of central importance in both the diagnosis and treatment of Alzheimer's and Parkinson's diseases. However, accurate high-throughput methods to detect and quantify oligomer populations are still needed. We present here a single-molecule approach for the detection and quantification of oligomeric species. The approach is based on the use of solid-state nanopores and multiplexed DNA barcoding to identify and characterize oligomers from multiple samples. We study α-synuclein oligomers in the presence of several small-molecule inhibitors of α-synuclein aggregation as an illustration of the potential applicability of this method to the development of diagnostic and therapeutic methods for Parkinson's disease.

Antipsychotic dose reduction and discontinuation versus maintenance treatment in people with schizophrenia and other recurrent psychotic disorders in England (the RADAR trial): an open, parallel-group, randomised controlled trial.

BACKGROUND: Maintenance antipsychotic medication is recommended for people with schizophrenia or recurrent psychosis, but the adverse effects are burdensome, and evidence on long-term outcomes is sparse. We aimed to assess the benefits and harms of a gradual process of antipsychotic reduction compared with maintenance treatment. Our hypothesis was that antipsychotic reduction would improve social functioning with a short-term increase in relapse. METHODS: RADAR was an open, parallel-group, randomised trial done in 19 National Health Service Trusts in England. Participants were aged 18 years and older, had a diagnosis of recurrent, non-affective psychotic disorder, and were prescribed an antipsychotic. Exclusion criteria included people who had a mental health crisis or hospital admission in the past month, were considered to pose a serious risk to themselves or others by a treating clinician, or were mandated to take antipsychotic medication under the Mental Health Act. Through an independent, internet-based system, participants were randomly assigned (1:1) to gradual, flexible antipsychotic reduction, overseen by treating clinicians, or to maintenance. Participants and clinicians were aware of treatment allocations, but assessors were masked to them. Follow-up was for 2 years. Social functioning, assessed by the Social Functioning Scale, was the primary outcome. The principal secondary outcome was severe relapse, defined as requiring admission to hospital. Analysis was done blind to group identity using intention-to-treat data. The trial is completed and has been registered with ISRCTN registry (ISRCTN90298520) and with ClinicalTrials.gov (NCT03559426). FINDINGS: 4157 people were screened, of whom 253 were randomly allocated, including 168 (66%) men, 82 (32%) women, and 3 (1%) transgender people, with a mean age of 46 years (SD 12, range 22-79). 171 (67%) participants were White, 52 (21%) were Black, 16 (6%) were Asian, and 12 (5%) were of other ethnicity. The median dose reduction at any point during the trial was 67% in the reduction group and zero in the maintenance group; at 24 months it was 33% versus zero. At the 24-month follow-up, we assessed 90 of 126 people assigned to the antipsychotic dose reduction group and 94 of 127 assigned to the maintenance group, finding no difference in the Social Functioning Scale (ß 0·19, 95% CI -1·94 to 2·33; p=0·86). There were 93 serious adverse events in the reduction group affecting 49 individuals, mainly comprising admission for a mental health relapse, and 64 in the maintenance group, relating to 29 individuals. INTERPRETATION: At 2-year follow-up, a gradual, supported process of antipsychotic dose reduction had no effect on social functioning. Our data can help to inform decisions about the use of long-term antipsychotic medication. FUNDING: National Institute for Health Research.

Optimization of a small molecule inhibitor of secondary nucleation in α-synuclein aggregation.

Parkinson's disease is characterised by the deposition in the brain of amyloid aggregates of α-synuclein. The surfaces of these amyloid aggregates can catalyse the formation of new aggregates, giving rise to a positive feedback mechanism responsible for the rapid proliferation of α-synuclein deposits. We report a procedure to enhance the potency of a small molecule to inhibit the aggregate proliferation process using a combination of in silico and in vitro methods. The optimized small molecule shows potency already at a compound:protein stoichiometry of 1:20. These results illustrate a strategy to accelerate the optimisation of small molecules against α-synuclein aggregation by targeting secondary nucleation.

Secondary Processes Dominate the Quiescent, Spontaneous Aggregation of α-Synuclein at Physiological pH with Sodium Salts.

The accurate recapitulation in an in vitro assay of the aggregation process of α-synuclein in Parkinson's disease has been a significant challenge. As α-synuclein does not aggregate spontaneously in most currently used in vitro assays, primary nucleation is triggered by the presence of surfaces such as lipid membranes or interfaces created by shaking, to achieve aggregation on accessible time scales. In addition, secondary nucleation is typically only observed by lowering the pH below 5.8. Here we investigated assay conditions that enables spontaneous primary nucleation and secondary nucleation at pH 7.4. Using 400 mM sodium phosphate, we observed quiescent spontaneous aggregation of α-synuclein and established that this aggregation is dominated by secondary processes. Furthermore, the presence of potassium ions enhanced the reproducibility of quiescent α-synuclein aggregation. This work provides a framework for the study of spontaneous α-synuclein aggregation at physiological pH.

Development of the HT&Me intervention to support women with breast cancer to adhere to adjuvant endocrine therapy and improve quality of life.

BACKGROUND: Breast cancer is the most common cancer in women worldwide. Approximately 80% of breast cancers are oestrogen receptor positive (ER+). Patients treated surgically are usually recommended adjuvant endocrine therapy (AET) for 5-10 years. AET significantly reduces recurrence, but up to 50% of women do not take it as prescribed. OBJECTIVE: To co-design and develop an intervention to support AET adherence and improve health-related quality-of-life (QoL) in women with breast cancer. METHODS: Design and development of the HT&Me intervention took a person-based approach and was guided by the Medical Research Council framework for complex interventions, based on evidence and underpinned by theory. Literature reviews, behavioural analysis, and extensive key stakeholder involvement informed 'guiding principles' and the intervention logic model. Using co-design principles, a prototype intervention was developed and refined. RESULTS: The blended tailored HT&Me intervention supports women to self-manage their AET. It comprises initial and follow-up consultations with a trained nurse, supported with an animation video, a web-app and ongoing motivational 'nudge' messages. It addresses perceptual (e.g. doubts about necessity, treatment concerns) and practical (e.g. forgetting) barriers to adherence and provides information, support and behaviour change techniques to improve QoL. Iterative patient feedback maximised feasibility, acceptability, and likelihood of maintaining adherence; health professional feedback maximised likelihood of scalability. CONCLUSIONS: HT&Me has been systematically and rigorously developed to promote AET adherence and improve QoL, and is complemented with a logic model documenting hypothesized mechanisms of action. An ongoing feasibility trial will inform a future randomised control trial of effectiveness and cost-effectiveness.

Exploration and Exploitation Approaches Based on Generative Machine Learning to Identify Potent Small Molecule Inhibitors of α-Synuclein Secondary Nucleation.

The high attrition rate in drug discovery pipelines is an especially pressing issue for Parkinson's disease, for which no disease-modifying drugs have yet been approved. Numerous clinical trials targeting α-synuclein aggregation have failed, at least in part due to the challenges in identifying potent compounds in preclinical investigations. To address this problem, we present a machine learning approach that combines generative modeling and reinforcement learning to identify small molecules that perturb the kinetics of aggregation in a manner that reduces the production of oligomeric species. Training data were obtained by an assay reporting on the degree of inhibition of secondary nucleation, which is the most important mechanism of α-synuclein oligomer production. This approach resulted in the identification of small molecules with high potency against secondary nucleation.

Optimized immunosuppression to prevent graft failure in renal transplant recipients with HLA antibodies (OuTSMART): a randomised controlled trial.

Background: 3% of kidney transplant recipients return to dialysis annually upon allograft failure. Development of antibodies (Ab) against human leukocyte antigens (HLA) is a validated prognostic biomarker of allograft failure. We tested whether screening for HLA Ab, combined with an intervention to improve adherence and optimization of immunosuppression could prevent allograft failure. Methods: Prospective, open-labelled randomised biomarker-based strategy (hybrid) trial in 13 UK transplant centres [EudraCT (2012-004308-36) and ISRCTN (46157828)]. Patients were randomly allocated (1:1) to unblinded or double-blinded arms and screened every 8 months. Unblinded HLA Ab+ patients were interviewed to encourage medication adherence and had tailored optimisation of Tacrolimus, Mycophenolate mofetil and Prednisolone. The primary outcome was time to graft failure in an intention to treat analysis. The trial had 80% power to detect a hazard ratio of 0.49 in donor specific antibody (DSA)+ patients. Findings: From 11/9/13 to 27/10/16, 5519 were screened for eligibility and 2037 randomised (1028 to unblinded care and 1009 to double blinded care). We identified 198 with DSA and 818 with non-DSA. Development of DSA, but not non-DSA was predictive of graft failure. HRs for graft failure in unblinded DSA+ and non-DSA+ groups were 1.54 (95% CI: 0.72 to 3.30) and 0.97 (0.54-1.74) respectively, providing no evidence of an intervention effect. Non-inferiority for the overall unblinded versus blinded comparison was not demonstrated as the upper confidence limit of the HR for graft failure exceeded 1.4 (1.02, 95% CI: 0.72 to 1.44). The only secondary endpoint reduced in the unblinded arm was biopsy-proven rejection. Interpretation: Intervention to improve adherence and optimize immunosuppression does not delay failure of renal transplants after development of DSA. Whilst DSA predicts increased risk of allograft failure, novel interventions are needed before screening can be used to direct therapy. Funding: The National Institute for Health Research Efficacy and Mechanism Evaluation programme grant (ref 11/100/34).

The effect of adherence therapy on medication adherence, health beliefs, self-efficacy, and depressive symptoms among patients diagnosed with major depressive disorder.

Medication adherence is a crucial health issue in major depressive disorder (MDD) that requires regular monitoring and attention. Hence, there are multiple reasons for medication non-adherence among them. This study aimed to examine the effect of adherence therapy (AT) on medication adherence, health beliefs, self-efficacy, and depressive symptoms among patients diagnosed with MDD. One group pretest-posttest, repeated measures time-series design was conducted. A sample of 32 patients was recruited conveniently; they received eight weekly sessions of AT. A self-reported questionnaire was used to measure variables. The analysis showed that the mean scores of the baseline indicated non-adherence, moderate general benefits beliefs about the medication, high beliefs that medication is harmful, high beliefs that doctors overuse medication, high beliefs about potential adverse effects from medication, low perception of MDD severity, and high threatening perception regarding MDD, a moderate degree of confidence in the ability to taking medications, and patients had moderately severe depressive symptoms (M = 16, 3.2, 3.1, 4.1, 3.8, 50, 3, 16 respectively). Over four measurement points, adherence therapy enhanced positive beliefs towards taking medication and illness, increased medication adherence self-efficacy, improved medication adherence, and decreased depressive symptoms (F = 68.57-379.2, P < 0.001). These improvements were clinically significant in all variables immediately post-AT but declined minimally over time. The study indicated that integrating AT as part of the pre-discharge protocol is one core component to sustaining positive healthcare outcomes. Continuous efforts should be paid in terms of the long-term sustainability of an intervention to enhance adherence and clinical outcomes.

Structure-Based Discovery of Small-Molecule Inhibitors of the Autocatalytic Proliferation of α-Synuclein Aggregates.

The presence of amyloid fibrils of α-synuclein is closely associated with Parkinson's disease and related synucleinopathies. It is still very challenging, however, to systematically discover small molecules that prevent the formation of these aberrant aggregates. Here, we describe a structure-based approach to identify small molecules that specifically inhibit the surface-catalyzed secondary nucleation step in the aggregation of α-synuclein by binding to the surface of the amyloid fibrils. The resulting small molecules are screened using a range of kinetic and thermodynamic assays for their ability to bind α-synuclein fibrils and prevent the further generation of α-synuclein oligomers. This study demonstrates that the combination of structure-based and kinetic-based drug discovery methods can lead to the identification of small molecules that selectively inhibit the autocatalytic proliferation of α-synuclein aggregates.

Validation of the Arabic version of medication adherence report scale questionnaire and beliefs about medication -specific questionnaire: A factor analysis study.

Medication Adherence Report Scale questionnaire (MARS-5) and the Beliefs about Medication Questionnaire-specific (BMQ-specific) are well known tools to assess adherence to medication and beliefs of chronic patients. However, validated Arabic versions of such questionnaires are lacking. We aim to validate the Arabic versions of BMQ-specific and MARS-5. In this study, a cross-sectional study was performed between November 2019 and March 2020. Participants were reached from secondary and tertiary care clinics in Jordan. Exploratory factor analysis (EFA) and Confirmatory Factor analysis (CFA) were conducted to validate the employed questionnaires on the tested sample. The internal consistency of the questionnaires was assessed by calculating Cronbach's alpha, and Cronbach's alpha if item is deleted. A total of 485 patients who met the inclusion criteria were recruited. The mean age of the participants was 57.14 years (22-82 years); and 39% of the participants were older than 65 years. The most common chronic diseases reported by participants were hypertension and diabetes mellitus, 35.7 and 32.2% respectively. EFA suggested two-factor model for BMQ-specific and one-factor model for MARS-5 which was confirmed by CFA analyses. The resulted Cronbach's alphas of the questionnaires ranged from 0.89-0.93. Both analyses showed that the Arabic versions of both MARS-5 and BMQ-specific are valid and can be used for the suggested study population. Further validation-based research may enhance the transcultural adaptation of such questionnaires.

A Systematic Review of Nudge Interventions to Optimize Medication Prescribing.

Background: The benefits of medication optimization are largely uncontroversial but difficult to achieve. Behavior change interventions aiming to optimize prescriber medication-related decisions, which do not forbid any option and that do not significantly change financial incentives, offer a promising way forward. These interventions are often referred to as nudges. Objective: The current systematic literature review characterizes published studies describing nudge interventions to optimize medication prescribing by the behavioral determinants they intend to influence and the techniques they apply. Methods: Four databases were searched (MEDLINE, Embase, PsychINFO, and CINAHL) to identify studies with nudge-type interventions aiming to optimize prescribing decisions. To describe the behavioral determinants that interventionists aimed to influence, data were extracted according to the Theoretical Domains Framework (TDF). To describe intervention techniques applied, data were extracted according to the Behavior Change Techniques (BCT) Taxonomy version 1 and MINDSPACE. Next, the recommended TDF-BCT mappings were used to appraise whether each intervention applied a sufficient array of techniques to influence all identified behavioral determinants. Results: The current review located 15 studies comprised of 20 interventions. Of the 20 interventions, 16 interventions (80%) were effective. The behavior change techniques most often applied involved prompts (n = 13). The MINDSPACE contextual influencer most often applied involved defaults (n = 10). According to the recommended TDF-BCT mappings, only two interventions applied a sufficient array of behavior change techniques to address the behavioral determinants the interventionists aimed to influence. Conclusion: The fact that so many interventions successfully changed prescriber behavior encourages the development of future behavior change interventions to optimize prescribing without mandates or financial incentives. The current review encourages interventionists to understand the behavioral determinants they are trying to affect, before the selection and application of techniques to change prescribing behaviors. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/], identifier [CRD42020168006].

Perceptions and Practicalities Influencing Pre-exposure Prophylaxis Adherence Among Men Who Have Sex with Men in England.

PrEP is highly effective for HIV prevention but requires adequate adherence. In this paper we use the perceptions and practicalities approach (PAPA) to identify factors that influenced PrEP adherence using qualitative data from the PROUD study. From February 2014 to January 2016, we interviewed 41 gay, bisexual and other men-who-have-sex-with-men and one trans woman who were enrolled in the study. We purposively recruited participants for interview based on trial arm allocation, adherence and sexual risk behaviours. The interviews were conducted in English, audio-recorded, transcribed, coded and analysed using framework analysis. Participants in general were highly motivated to use and adhere to PrEP, and this was linked to strong perceptions of personal necessity for PrEP as they felt at risk of HIV and viewed PrEP as highly effective. On the other hand, concerns about side effects and HIV resistance did inhibit PrEP initiation and adherence although this was uncommon. Practical factors such as daily routine, existing habitual pill-taking and pill storage impacted adherence. Drug and alcohol use rarely caused participants to miss doses. These findings indicate that using the principals of PAPA to unpick influencers of PrEP use, could help tailor adherence support in PrEP programmes.