Patients as research partners in preference studies: learnings from IMI-PREFER.

BACKGROUND: There is growing recognition of the importance of patient and public stakeholder involvement (PPI) in patient preference research. However, limited evidence exists regarding the impact, barriers and enablers of PPI in preference studies. The Innovative Medicines Initiative (IMI)-PREFER project conducted a series of preference case studies which incorporated PPI. OBJECTIVE: To describe: (1) how PPI was operationalized in the PREFER case studies, (2) the impact of PPI, and (3) factors that served to impede and facilitate PPI. METHODS: We reviewed the PREFER final study reports to determine how patient partners were involved. We conducted a thematic framework analysis to characterize the impact of PPI and then administered a questionnaire to the PREFER study leads to identify barriers and facilitators to effective PPI. RESULTS: Eight PREFER case studies involved patients as research partners. Patient partners were involved in activities spanning all phases of the patient preference research process, including in study design, conduct and dissemination. However, the type and degree of patient partner involvement varied considerably. Positive impacts of PPI included improvements in the: (1) quality of the research and research process; (2) patient partner empowerment; (3) study transparency and dissemination of results; (4) research ethics, and (5) trust and respect between the research team and the patient community. Of the 13 barriers identified, the 3 most frequently reported were inadequate resources, insufficient time to fully involve patient partners, and uncertainty regarding how to operationalize the role of 'patient partner. Among the 12 facilitators identified, the two most frequently cited were (1) having a clearly stated purpose for involving patients as research partners; and (2) having multiple patient partners involved in the study. CONCLUSION: PPI had many positive impacts on the PREFER studies. Preference study leads with prior PPI experience reported a greater number of positive impacts than those with no such experience. In light of the numerous barriers identified, multi-faceted implementation strategies should be considered to support adoption, integration and sustainment of PPI within preference research. Additional case studies of patient partner involvement in preference research are needed as well to inform best practices in this area.

Research about patients' preferences for medicinal products and treatments is growing. Such research could be improved if patients were involved as 'research partners,' that is, as active members of the study team itself. To date, however, little is known about the actual experience of involving patients as partners in such research. This paper presents learnings from involving patients as partners in 8 case studies conducted as part of IMI-PREFER, a big, European-based project which aimed to develop recommendations about how to conduct preference research. Involving patients as partners led to improvements in the: (1) quality of the research and research process; (2) recruitment of participants; (3) content and design of patient-facing informational materials; and, (4) how and what study results were shared with patient communities. Our findings showed that it is important to plan for patient partners' involvement early on in the design of the preference study so as to ensure that they are fully integrated into the research team and their opportunity to contribute to all stages of the research is optimized. Such planning should address how patient partners will be paid, what their role responsibilities will include, how and when they will be trained and educated, and how they will be supported throughout the course of the study. Having a clearly stated purpose for involving patients as research partners, selecting patient partners who have had prior research experience and relationships with the researchers, and having multiple patient partners on the study team are all also helpful in supporting successful patient involvement. We need more people to share their experiences with involving patient partners in preference research so that we can continue to improve how this is done.

Estimating conformational landscapes from Cryo-EM particles by 3D Zernike polynomials.

The new developments in Cryo-EM Single Particle Analysis are helping us to understand how the macromolecular structure and function meet to drive biological processes. By capturing many states at the particle level, it is possible to address how macromolecules explore different conformations, information that is classically extracted through 3D classification. However, the limitations of classical approaches prevent us from fully understanding the complete conformational landscape due to the reduced number of discrete states accurately reconstructed. To characterize the whole structural spectrum of a macromolecule, we propose an extension of our Zernike3D approach, able to extract per-image continuous flexibility information directly from a particle dataset. Also, our method can be seamlessly applied to images, maps or atomic models, opening integrative possibilities. Furthermore, we introduce the ZART reconstruction algorithm, which considers the Zernike3D deformation fields to revert particle conformational changes during the reconstruction process, thus minimizing the blurring induced by molecular motions.

On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy.

Cryo-electron microscopy (cryoEM) has become a well established technique to elucidate the 3D structures of biological macromolecules. Projection images from thousands of macromolecules that are assumed to be structurally identical are combined into a single 3D map representing the Coulomb potential of the macromolecule under study. This article discusses possible caveats along the image-processing path and how to avoid them to obtain a reliable 3D structure. Some of these problems are very well known in the community. These may be referred to as sample-related (such as specimen denaturation at interfaces or non-uniform projection geometry leading to underrepresented projection directions). The rest are related to the algorithms used. While some have been discussed in depth in the literature, such as the use of an incorrect initial volume, others have received much less attention. However, they are fundamental in any data-analysis approach. Chiefly among them, instabilities in estimating many of the key parameters that are required for a correct 3D reconstruction that occur all along the processing workflow are referred to, which may significantly affect the reliability of the whole process. In the field, the term overfitting has been coined to refer to some particular kinds of artifacts. It is argued that overfitting is a statistical bias in key parameter-estimation steps in the 3D reconstruction process, including intrinsic algorithmic bias. It is also shown that common tools (Fourier shell correlation) and strategies (gold standard) that are normally used to detect or prevent overfitting do not fully protect against it. Alternatively, it is proposed that detecting the bias that leads to overfitting is much easier when addressed at the level of parameter estimation, rather than detecting it once the particle images have been combined into a 3D map. Comparing the results from multiple algorithms (or at least, independent executions of the same algorithm) can detect parameter bias. These multiple executions could then be averaged to give a lower variance estimate of the underlying parameters.

Cryo-EM and Single-Particle Analysis with Scipion.

Cryo-electron microscopy has become one of the most important tools in biological research to reveal the structural information of macromolecules at near-atomic resolution. In single-particle analysis, the vitrified sample is imaged by an electron beam and the detectors at the end of the microscope column produce movies of that sample. These movies contain thousands of images of identical particles in random orientations. The data need to go through an image processing workflow with multiple steps to obtain the final 3D reconstructed volume. The goal of the image processing workflow is to identify the acquisition parameters to be able to reconstruct the specimen under study. Scipion provides all the tools to create this workflow using several image processing packages in an integrative framework, also allowing the traceability of the results. In this article the whole image processing workflow in Scipion is presented and discussed with data coming from a real test case, giving all the details necessary to go from the movies obtained by the microscope to a high resolution final 3D reconstruction. Also, the power of using consensus tools that allow combining methods, and confirming results along every step of the workflow, improving the accuracy of the obtained results, is discussed.

Patient Preferences in Rare Diseases: A Qualitative Study in Neuromuscular Disorders to Inform a Quantitative Preference Study.

INTRODUCTION: It has become increasingly important to include patient preference information in decision-making processes for drug development. As neuromuscular disorders represent multisystem, debilitating, and progressive rare diseases with few treatment options, this study aimed to explore unmet health care needs and patient treatment preferences for two neuromuscular disorders, myotonic dystrophy type 1 (DM1) and mitochondrial myopathies (MM) to inform early stages of drug development. METHODS: Fifteen semi-structured interviews and five focus group discussions (FGDs) were held with DM1 and MM adult patients and caregivers. Topics discussed included (1) reasons for study participation; (2) disease signs/symptoms and their impact on daily lives; (3) top desired benefits; and (4) acceptability of risks and tolerance levels for a hypothetical new treatment. Data were analyzed following a thematic 'code' approach. RESULTS: A total of 52 participants representing a wide range of disease severities participated. 'Muscle strength' and 'energy and endurance' were the disease-related unmet needs most often mentioned. Additionally, improved 'balance', 'cognition' and 'gut function' were the top desired treatment benefits, while 'damage to the liver, kidneys or eyes' was the most concerning risk. Factors influencing their tolerance to risks related to previously having experienced the risk and differentiation between permanent and temporary risks. A few differences were elicited between patients and caregivers. CONCLUSIONS: This qualitative study provided an open forum to elicit treatment-desired benefits and acceptable risks to be established by patients themselves. These findings can inform decisions for developing new treatments and the design of clinical trials for DM1 and MM.

DeepAlign, a 3D alignment method based on regionalized deep learning for Cryo-EM.

Cryo Electron Microscopy (Cryo-EM) is currently one of the main tools to reveal the structural information of biological specimens at high resolution. Despite the great development of the techniques involved to solve the biological structures with Cryo-EM in the last years, the reconstructed 3D maps can present lower resolution due to errors committed while processing the information acquired by the microscope. One of the main problems comes from the 3D alignment step, which is an error-prone part of the reconstruction workflow due to the very low signal-to-noise ratio (SNR) common in Cryo-EM imaging. In fact, as we will show in this work, it is not unusual to find a disagreement in the alignment parameters in approximately 20-40% of the processed images, when outputs of different alignment algorithms are compared. In this work, we present a novel method to align sets of single particle images in the 3D space, called DeepAlign. Our proposal is based on deep learning networks that have been successfully used in plenty of problems in image classification. Specifically, we propose to design several deep neural networks on a regionalized basis to classify the particle images in sub-regions and, then, make a refinement of the 3D alignment parameters only inside that sub-region. We show that this method results in accurately aligned images, improving the Fourier shell correlation (FSC) resolution obtained with other state-of-the-art methods while decreasing computational time.

Algorithmic robustness to preferred orientations in single particle analysis by CryoEM.

The presence of preferred orientations in single particle analysis (SPA) by cryo-Electron Microscopy (cryoEM) is currently one of the hurdles preventing many structural analyses from yielding high-resolution structures. Although the existence of preferred orientations is mostly related to the grid preparation, in this technical note, we show that some image processing algorithms used for angular assignment and three-dimensional (3D) reconstruction are more robust than others to these detrimental conditions. We exemplify this argument with three different data sets in which the presence of preferred orientations hindered achieving a 3D reconstruction without artifacts or, even worse, a 3D reconstruction could never be achieved.

Integration of Cryo-EM Model Building Software in Scipion.

Advances in cryo-electron microscopy (cryo-EM) have made it possible to obtain structures of large biological macromolecules at near-atomic resolution. This "resolution revolution" has encouraged the use and development of modeling tools able to produce high-quality atomic models from cryo-EM density maps. Unfortunately, many practical problems appear when combining different packages in the same processing workflow, which make difficult the use of these tools by non-experts and, therefore, reduce their utility. We present here a major extension of the image processing framework Scipion that provides inter-package integration in the model building area and full tracking of the complete workflow, from image processing to structure validation.

Re-examining the spectra of macromolecules. Current practice of spectral quasi B-factor flattening.

The analysis of structure factors in 3D cryo-EM Coulomb potential maps and their "enhancement" at the end of the reconstruction process is a well-established practice, normally referred to as sharpening. The aim is to increase contrast and, in this way, to help tracing the atomic model. The most common way to accomplish this enhancement is by means of the so-called B-factor correction, which applies a global filter to boost high frequencies with some dampening considerations related to noise amplification. The results are maps with a better visual aspect and a quasiflat spectrum at medium and high frequencies. This practice is so widespread that most map depositions in the Electron Microscopy Data Base (EMDB) only contain sharpened maps. Here, the use in cryoEM of global B-factor corrections is theoretically and experimentally analyzed. Results clearly illustrate that protein spectra present a falloff. Thus, spectral quasi-flattening may produce protein spectra with distortions when compared with experimental ones, this fact, combined with the practice of reporting only sharpened maps, generates a sub-optimal situation in terms of data preservation, reuse and reproducibility. Now that the field is more advanced, we put forward two suggestions: (1) to use methods which keep more faithfully the original experimental signal properties of macromolecules when "enhancing" the map, and (2) to further stress the need to deposit the original experimental maps without any postprocessing or sharpening, not only the enhanced maps. In the absence of access to these original maps data is lost, preventing their future analysis with new methods.

Flexible workflows for on-the-fly electron-microscopy single-particle image processing using Scipion.

Electron microscopy of macromolecular structures is an approach that is in increasing demand in the field of structural biology. The automation of image acquisition has greatly increased the potential throughput of electron microscopy. Here, the focus is on the possibilities in Scipion to implement flexible and robust image-processing workflows that allow the electron-microscope operator and the user to monitor the quality of image acquisition, assessing very simple acquisition measures or obtaining a first estimate of the initial volume, or the data resolution and heterogeneity, without any need for programming skills. These workflows can implement intelligent automatic decisions and they can warn the user of possible acquisition failures. These concepts are illustrated by analysis of the well known 2.2 Šresolution ß-galactosidase data set.

Development of a Pediatric Goal-Centered Upper Limb Spasticity Home Exercise Therapy Program for Use in a Phase-III Trial of Abobotulinumtoxina (Dysport®).

AIMS: To create a standardized home exercise therapy program that could be implemented by international sites to provide a consistent level of therapeutic intervention for pediatric patients participating in an ongoing Phase-III, randomized, controlled trial of repeat abobotulinumtoxinA injections for pediatric upper limb spasticity (NCT02106351). METHODS: Physical therapists, occupational therapists, and medical doctors worked collaboratively to design an exercise therapy program to be implemented in the home setting. In this article, we describe the development process and the finalized program that is currently being used in the Phase-III trial. RESULTS: The final program is presented as a "toolbox" for therapists, and includes a standardized step-wise process for choosing the most appropriate exercises and functional activities to achieve the agreed treatment goals of each abobotulinumtoxinA injection. The core toolbox includes: a clear protocol for clinicians, information sheets, signature of commitment forms, exercise score charts, and the library of exercises and functional activities that therapists choose from to aid the patient in achieving their treatment goals. CONCLUSIONS: Implementation of this home therapy program provides a standardized background of good practice against which to test the efficacy of abobotulinumtoxinA. Preliminary data show that the program is readily accepted by patients and their families.

Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead.

Electron cryomicroscopy (cryoEM) is essential for the study and functional understanding of non-crystalline macromolecules such as proteins. These molecules cannot be imaged using X-ray crystallography or other popular methods. CryoEM has been successfully used to visualize macromolecular complexes such as ribosomes, viruses, and ion channels. Determination of structural models of these at various conformational states leads to insight on how these molecules function. Recent advances in imaging technology have given cryoEM a scientific rebirth. As a result of these technological advances image processing and analysis have yielded molecular structures at atomic resolution. Nevertheless there continue to be challenges in image processing, and in this article we will touch on the most essential in order to derive an accurate three-dimensional model from noisy projection images. Traditional approaches, such as k-means clustering for class averaging, will be provided as background. We will then highlight new approaches for each image processing subproblem, including a 3D reconstruction method for asymmetric molecules using just two projection images and deep learning algorithms for automated particle picking.

Falls and resulting fractures in Myotonic Dystrophy: Results from a multinational retrospective survey.

Myotonic Dystrophy type 1 multisystem involvement leads to functional impairment with an increased risk of falling. This multinational study estimates the prevalence of falls and fall-associated fractures. A web-based survey among disease-specific registries (Germany, UK and The Netherlands) was carried out among DM1 ambulant adults with a total of 573 responses retrieved. Results provided a risk ratio estimation of 30%-72% for falls and of 11%-17% for associated fractures. There was no significant difference for falls between male and female, but there was for fall-related fractures with a higher prevalence in women. Balance and leg weakness were the most commonly reported causes for falling. This study is based on a voluntary retrospective survey with naturally inherent limitations; however, the sample size allows for robust comparisons. The estimated risk of falls in this cohort with a mean age of 46 years compares to a previous estimation for a healthy population of over 65 years of age. These results suggest a premature-ageing DM1 phenotype with an increased risk of falling depending on age and disease severity that, so far, might have been underestimated. This may have clinical implications for the development of care guidelines and when testing new interventions in this population.