Background: Electronic devices for measuring blood pressure (BP) need to go through independent clinical validation as recommended by different authorities, both in general and specific populations. The aim of this study was to assess the validity of the Omron RS6 (HEM-6221-E) wrist oscillometric devices in obese Sudanese patients.Methods: Of 90 obese individuals invited for recruitment, 33 were included in the study, and had their BP at the level of the wrist measured using Omron RS6 and standard mercury sphygmomanometer. Two observations were made and the mean was taken. BP differences between the two methods for the 33 participants were classified into three categories , according to the European Society of Hypertension-International Protocol revision 2010 (ESH-IP2) criteria. This was then used to assess the validity of the tested Omron RS6 device.Results: Participants had a mean age of 56.97 years (standard deviation (SD), 8.75; range, 36-79). Average systolic blood pressure (SBP) was 146.21 mmHg , and average diastolic blood pressure (DBP) was 93.82 mmHg . There was a good agreement between the two observations using the OMRON RS6 and the standard sphygmomanometer: −4 to + 3 mmHg for SBP and −4 to +4 mmHg for DBP, with the mean difference of 1.73±1.11 mmHg for SBP and 1.49±1.02 mmHg for DBP.Conclusion: Thus, the Omron RS6 (HEM-6221-E) is a valid and suitable measure of BP according to ESH-IP2. It is a global public health issue, and is recognized to be the major factor contributing to the burden of heart disease, stroke, kidney failure and premature mortality and disability all over the world. In spite of this burden, a simple preventive and non-invasive measure such as checking and monitoring blood pressure (BP) is expected to decrease the associated cardiovascular mortality with hypertension dramatically. Hypertension is mostly symptomless particularly in the early stages unless there are complications or other reasons to reveal it, which is why many people go undiagnosed. Moreover, those who are diagnosed may not have access to treatment and may not be able to successfully control their hypertension long term. In order to ensure that the disease is well-controlled, BP should be monitored regularly.Hypertension is defined as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg. Therefore, these electronic devices must be of certain accuracy. As a result, many BP measuring devices have been validated to meet international standards for the use by the general population. However, few studies for the validation of electronic devices in specific groups, such as the elderly, infants and obese individuals, have been done.Accurate BP readings via a reliable device is equally important, since this will influence diagnosis and treatment. Generally, there are electronic, mercury and aneroid devices that are used to measure BP. The World Health Organization recommends the use of affordable and reliable electronic devices that have the option to select manual readings, because mercury is toxic and aneroid devices need calibrations and trained personnel for use. Secondly, obesity affects individuals in very different ways, and many obese people have an significantly increased arm circumference. Hence, if BP is not measured with an appropriately sized cuff it will lead to false high readings, which may conduce unnecessary treatment. In order to solve the problem of adiposity, some studies use the forearm instead of the arm to measure BP in obese individuals, which results in overestimated BP by approximately 7-15 mmHg – this has to be corrected with equations. Recently, a study targeting obese individuals compared the BP readings using two devices, one at the level of the arm  and the other at the wrist level  to a standard method. These authors found that the electronic device's readings varied significantly from the readings obtained by the standard sphygmomanometer and did not meet the required criteria for obese adults. In this study, we aimed to evaluate BP measurements among obese hypertensive Sudanese patients using the radial artery (wrist level) compared to the standard brachial measured using a mercury sphygmomanometer and non-electronic stethoscope to attain more valuable information about the validity of using such a method.For overweight and obese individuals, BP measurement is more challenging. First, obesity itself plays a major role in elevating BP through increasing insulin resistance and creating a cycle that eventually ends to metabolic syndromeOmron RS6 (HEM-6221-E) is a fully automated device for self/home BP measurement at the wrist level using the oscillometric method. Inﬂation is by automated fuzzy logic–controlled electric pump and deﬂation is by automatic pressure release valve. It measures 87 mm × 64 mm × 14 mm (width × height × depth). It has a single wrist cuff, which can be used for wrist size between 13.5–21.5 cm. The device demonstrates the pulse and BP (SBP and DBP) in a digital liquid crystal screen. The device can detect a BP range from 0 to 299 mmHg and pulse rate from 40 to 180 beats/min. Additionally, it can save the last 90 BP readings and it has a position sensor for the wrist and the ability to detect irregular pulse and body motion.2), adult (age ≥25 years), male and female hypertensive patients, able to give informed consent. BMI was computed from measured weight and height using standard methods. Individuals with abnormal rhythm or uncertainly DBP were excluded.A cross-sectional study was conducted at Gadarif Hospital outpatient clinic in eastern Sudan. Patients who fulfilled the following inclusion criteria were enrolled in the present study: obese (body mass index (BMI) ≥30 kg/mThis study was conducted among the obese Sudanese population. A total of 90 participants volunteered to take part in the study and met the inclusion criteria, with at least 30 men and 30 women. Participants were invited to take part in the study during their regular doctor’s appointment.Participants were recruited to ensure a uniform distribution of test pressures across the BP range: 90–180 mmHg for SBP and 40–130 mmHg for DBP. The participants were divided into three groups as per their SBP and DBP readings: SBP – low (90–129), medium (130–160), and high (161–180); for DBP – low (40–79), medium (80–100) and high (101–130). Readings outside these ranges were included and were either categorized as low or high. In each of the three SBP and DBP ranges, a minimum of 10 to 12 patient was included according to the European Society of Hypertension-International Protocol revision 2010 (ESH-IP2) guidelines with at least 10 men and 10 women in the sample size . Procedure. In the outpatient clinic, BP measurements were taken in a room with a comfortable temperature, and ambient noise kept to minimum to avoid disruption during auscultation. Patients who fulfilled the inclusion criteria were allowed to rest for 10 minutes sitting on a chair in upright position with their legs uncrossed and back supported, with complete exposure of the left arm and forearm (at the time of BP checking) so there were no intervening clothes between the cuff and the arm, which might reduce the blood flow. Participants were encouraged to avoid talking or using mobile phones. The arm circumference was checked, and the suitable cuff applied. All the measurements were obtained from the left arm at the heart level.BP measurements were obtained first by the standard mercury sphygmomanometer then alternating with the test device, so that we obtained nine consecutive BP measurements from each participant: five measurements from the standard mercury sphygmomanometer and four measurements from the tested device.Blood pressure measurements. The manufacturer of the Omron RS6 (HEM-6221-E) was asked to provide a standard model, and the automated device was used after familiarization sessions in the outpatient clinic for one week, during which time the team performed 12 test measurements and accustomed themselves to the device and the study. No problems in this familiarization period were encountered. For the standard test, two stethoscopes and two mercury sphygmomanometers with different cuff sizes were carefully checked prior to the study.The working team composed of three personnel, two observers and a supervisor, well trained in BP measurement using standard mercury sphygmomanometer and stethoscope with well-fitting earpieces. They were of good health, hearing, and sight and able to follow the menisci at eye level from 40 mmHg to 180 mmHg. The two observers measured BP using two mercury sphygmomanometer and these readings were used as a reference. Observers were blinded from each other’s readings and from the device readings. They took BP measurement simultaneously and record them to the nearest 2 mmHg. The supervisor checked the agreement of BP values between the two observers so that any readings that varied by 4 mmHg or more were repeated. The supervisor measured the BP using the test wrist device. Two observations were made.Before measuring BP, the observer measured the arm circumference in order to use the appropriate size cuff for the standard mercury sphygmomanometer: small cuff for an arm circumference 17–21.9 cm, a regular cuff for an arm circumference of 22–31.9 cm, a large cuff for an arm circumference of 32–42 cm and for patients with arm circumference more than 42 cm an extra-large cuff is used. was used as a guide for data analysis. Accordingly, data was analyzed, presented and expressed to assess the ability of the device to pass or fail to pass the validation protocol requirements. SPSS (version 22) and Microsoft Excel software were used to perform all data analysis. Measurements obtained by the two observers for the standard device were compared and their average was taken, which was later compared with the test device SBP and DBP. The numbers of differences for the tested device and the observer using mercury sphygmomanometer within 5, 10, and 15mmHg, were calculated for both SBP and DBP then the average differences of the values with the standard deviation between the SBP and DBP of the mercury and the tested device were obtained. Finally, Bland–Altman plots were performed for both SBP and DBP to show the differences of device-observer versus average device and observer values for all the 99 pairs of comparisons.The ESH-IP2Ethical approval was received from the Ethics Committee at the Faculty of Medicine, Gadarif University, Sudan (reference number: 2017/09). Written informed consent to participate was collected from each participant before taking part in the research.In total, 90 obese participants were screened for inclusion into this study, 57 were excluded . Therefore, data related to 33 participants (12 men and 21 women) who fulfilled the requirements and the criteria of the ESH-IP protocol were analyzed.Mean (±standard deviation) age of participants was 56.97±8.75 years ; mean wrist circumference was 22.58±3.25 ; mean height was 163.52±13.54 cm ; and mean BMI of 38.74±8.26 . The mean BP values for SBP was 146.21±23.07 mmHg  and for DBP was 93.82±16.06 mmHg . Respondents' characteristics and BP measurements are summarized inThere was adequate agreement between the tested wrist BP measuring device (Omron RS6 (HEM-6221-E)) and the mercury sphygmomanometer . All theIn this study, the Bland-Altman plot showing the observers differences in SBP and SBP reveal that there is adequate agreement between the tested wrist BP measuring device (Omron RS6 (HEM-6221-E)) and the mercury sphygmomanometer . The SBP also showed that the OMRON RS7 device fulfils the validation criteria of ESH-IP validation protocol in two independent study centres among subjects, showing inter-centre reproducibility.A very recent study, however another study reported that the device failed to fulfil the ESH-IP revision 2010 requirements among obese subjects. Deutschet al. used the Omron RS6 position sensor with PSON or PSOFF, which showed that the position sensor is important for the function of the device at the wrist level and it improves the accuracy of the measurements by decreasing variations in wrist height.In our study, the screening process to include and exclude participants was time consuming. In addition, there was a difficulty in recruiting participants with high blood pressure. According to the validation protocol of ESH-IP, three studies have already been carried out to confirm the validity of the Omron RS6 (HEM-6221-E) device. Validation among the general population confirmed the general validity of the deviceThis study included only participants meaning that extrapolation of the results should be done with caution to individual who are not obese. Moreover, device accuracy needs to be adjusted to wrist circumference and position during measurement.The tested device, Omron RS6 (HEM-6221-E), achieved all the required standards for self/home measurement of blood pressure at the wrist level set by the ESH-IP, and accordingly would safely be recommended for personal use at home among obese patients provided that the manufacturer’s instructions are followed.https://doi.org/10.17605/OSF.IO/7S5D3 (registered on 18th October 2020: osf.io/w7dtn).Open Science Framework: Validation of the wrist blood pressure measuring device Omron RS6 (HEM-6221-E) among obese Sudanese patients compared with a standard mercury sphygmomanometer: a cross-sectional study according to the European Society of Hypertension International Protocol Revision 2010,Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of the This study investigated the validity of the Omron RS6 (HEM-6221-E) wrist oscillometric devices in the blood pressure monitoring for 33 Obese Sudanese patients. This manuscript is well written and obtained results confirming their hypothesis. However, there are several studies that blood pressure levels were different between the level of the arm and that of the wrist. It should be noted that, based on much evidence, blood pressure is recommended to be measured in the upper arms, using an appropriate cuff size for the arm circumference. In the case of home blood pressure monitoring for obese patients, blood pressure should be measured at the level of the arm  using a larger cuff.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:NAI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The article presents a successful validation of a blood pressure monitor in an obese population. It satisfies the methodological requirements for a validation study and is written clearly and succinctly. It is to be noted (as the authors do) that a validation study of the same monitor in an equivalent population has been previously conducted and found the monitor failed validation. This is perfectly reasonable in itself, but perhaps raises the question of why the authors felt another validation study was warranted, and if they have any insights into the reason for the difference in results. Arguably related to this, some of the discussion of previous studies  should be in the background.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Blood pressure monitoringI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


The purpose of our study was to determine the willingness to be vaccinated against COVID-19 and factors associated with vaccine hesitancy among people with epilepsy (PWE). In December 2020, we performed an online cross-sectional survey of PWE and their caregivers in Lithuania before the rollout of COVID-19 vaccines to the public. The study sample consisted of 111 respondents (44 (39.6%) male, median age 25 years (range 1 to 70)). From 58 PWE who personally responded to the survey, 27 (46.6%) would be willing to be vaccinated against COVID-19. Among the 53 caregivers, 18 (34.0%) would accept the person they care for to be vaccinated. Willingness to be vaccinated was associated with receiving an influenza shot in 2020 (odds ratio (OR) = 9.17, 95% confidence interval (CI = 1.15–73.47), the beliefs that vaccines are generally safe  and that they are the only convenient way to gain immunity . Respondents were hesitant to accept the COVID-19 vaccine if they thought it could cause the infection . COVID-19 vaccine hesitancy is frequent among PWE and their caregivers. It is probably related to erroneous beliefs about their safety and mechanism of action. The global threat of COVID-19 has greatly influenced the daily lives and health status of people with epilepsy (PWE). A deterioration of their physical and psychological condition was mostly associated with a lack of timely medical services, disrupted use or supply of antiseizure drugs (ASDs) and the consequences of strict lockdown measures, which induced mental health problems and were associated with seizure exacerbation ,3,4,5. WWe conducted an online cross-sectional anonymous survey among PWE and their caregivers in Lithuania from 7 December to 31 December 2020. The questionnaire  was creaThe survey was divided into three parts: (1) general and clinical characteristics of PWE, (2) COVID-19 infections among PWE and (3) the respondents’ outlook on vaccines. While most adult (≥18 years old) PWE indicated their personal outlook on vaccination, caregivers were asked to provide information about the person they care for as their legal guardians . That is, caregivers specified clinical information about the patient, but reported their own view on vaccination and any decisions they would make as the patient’s representative.No personal information  was collected during the study. The respondents were informed that participation in the survey was greatly appreciated, that only anonymous data were collected, and that the completion of the questionnaire would take up to ten minutes. To avoid missing data, it was required to answer each question before submission. All completed surveys were used for analysis (the only exclusion criteria was being <18 years old if self-identified as a PWE). According to local regulations, such an anonymous online survey did not require approval from the Regional Bioethics Committee.All statistical analysis was conducted in Microsoft Excel 16.0 (Microsoft) and SPSS Statistics 23.0 (IBM). Categorical data were compared by using Chi-square and Fisher’s exact tests. Ordinal and non-normally distributed continuous variables were analyzed by using Mann–Whitney U and Kruskal–Wallis tests (comparison of independent samples) and Spearman’s rho (correlation). Wilcoxon’s rank test was used when comparing the rate of seizures in the summer of 2020  and seizure frequency in December 2020 . Only the rates of those persons who experience at least several seizures per month were comparable in this part of the analysis. A binary logistic regression model was created to determine relevant variables associated with the willingness to be vaccinated against COVID-19.p < 0.0001)—just six patients (11.3%) represented in the survey by their caregivers were adults (≥18 years). Among all patients, 38 (34.2%) were on ASD monotherapy and 68 (61.3%) were on polytherapy . After a consultation by a specialist in neurology, the ASD regimen was changed for 47 (42.3%) patients during the COVID-19 pandemic. In eight (7.2%) cases, the use of ASDs was modified by the patients themselves or their caregivers with no supervision of the treating neurologist.General and clinical characteristics of PWE  are presented in n = 57, 51.4%), seizure frequency was not significantly different in the summer of 2020 as opposed to December 2020 . Overall, eight (14.0%) of these individuals reported higher seizure frequency and eight (14.0%) indicated a lower rate of their seizures during summer.Among persons who experience at least several seizures per month (n = 6), fatigue (n = 5) or headache (n = 5). One patient indicated an increase in seizure frequency (by less than two times) when being ill with COVID-19, while others experienced seizures at a usual rate. None were hospitalized or changed their ASD regimen when being ill; one respondent was consulted by an epileptologist and went to the emergency department.Eight (7.2%) persons with epilepsy in our study were diagnosed with COVID-19 and experienced mainly fever , seizure type  or being infected with COVID-19 . It was related to the number of ASDs the patient uses  and significantly higher in the group that would agree with vaccination against COVID-19 . The willingness to be vaccinated for free did not differ in a statistically significant way depending on seizure type , sex , age , ASD number  or seizure frequency . Among written reasons for declining vaccination, individual respondents indicated that “the vaccine against COVID-19 is not yet fully explored for serious side-effects”, “there was little time for comprehensive research of the vaccine”. They also doubt that the vaccine is effective or know someone who had “severe complications” or “went to an intensive care unit” after vaccination against other infectious diseases. From the eight participants who already had COVID-19, two would agree to be vaccinated, three would refuse because of natural immunity and three—because of potential side-effects of the vaccine.The score  of how much participants are waiting for a COVID-19 vaccine  was not A binary logistic regression model  identifiMost often, the primarily selected source of information regarding COVID-19 vaccines was reported to be specialists in neurology or epileptology  and general practitioners .A similar number of both persons with epilepsy and caregivers responded to our survey. Persons with various seizure types and seizure frequencies were included, most used at least one ASD—it may therefore be assumed that the sample is representative of patients with active epilepsy. Our study could not confirm significant seizure exacerbation during the study period  among those who frequently experience seizures. This may be explained by the adaptation of the healthcare system to ensure the availability of ASDs as well as patient adaptation to lockdown measures during a second wave of the pandemic in December 2020. Further, the results indicate great compliance with the ASD regimen and reveal that patients almost never modified the use of their medication without supervision—this might have also prevented seizure exacerbation ,10.Vaccination programs against COVID-19 started in various countries towards the end of 2020—with the emergence of effective COVID-19 vaccines, there still remain societal phenomena, namely vaccine hesitancy, that could significantly reduce the efficacy of COVID-19 mass-vaccination programs . They maA binary logistic regression analysis revealed that a general belief in vaccine safety and an understanding of how vaccines function  greatly increases the odds ratio that the study participant would prefer vaccination. Further, being vaccinated against influenza in 2020 was also associated with vaccine acceptance, as has already been noted in other settings ,23. In LWe asked to provide a binary and definite (yes/no) answer about the will to be vaccinated—some persons, however, could still be undecided and swayed towards vaccination if additional information was provided . While aOur study was cross-sectional and based online, therefore, limited because of non-respondent bias as well as inclusion of only those patients and caregivers who are members or followers of online epilepsy groups. Further, there were no mass-vaccination and related awareness campaigns in the country (before or during the study period), which could lead to an underestimation of the COVID-19 vaccine uptake potential among study participants. As vaccine hesitancy is context-specific and greatly depends on cultural, societal, and economic factors, the study needs to be replicated in different settings to substantiate our findings. Further studies could additionally investigate whether baseline knowledge about COVID-19 and societal factors  are associated with vaccine hesitancy among PWE. Finally, the lack of a control group does not allow for direct comparison with the general population and a relatively small sample size may reduce the generalizability of the results.In the setting of an online survey, the majority of adult PWE and caregivers (representing mostly children with epilepsy) expressed reluctancy to be vaccinated against COVID-19. Prior vaccination history, positive outlook on the safety profile of vaccines and an understanding of how they function were the most important factors associated with the willingness to be vaccinated. Neurologists are seen as a primary source of information regarding COVID-19 vaccines and might have an important role in counteracting vaccine hesitancy among PWE.


Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and comprehensive transcriptome data resource can be used to build a better DDI prediction model is still unclear. Therefore, we developed and validated a novel deep learning model for predicting DDI using 89,970 known DDIs extracted from the DrugBank database (version 5.1.4).sulfonylureas to cause hypoglycemia and drugs interacting with metformin to cause lactic acidosis, and showed both to induce effects on the proteins involved in the metabolic mechanism in vivo.The proposed model consists of a graph convolutional autoencoder network (GCAN) for embedding drug-induced transcriptome data from the L1000 database of the LINCS project; and a long short-term memory (LSTM) for DDI prediction. Comparative evaluation of various machine learning methods demonstrated the superior performance of our proposed model for DDI prediction. Many of our predicted DDIs were revealed in the latest DrugBank database (version 5.1.7). In the case study, we predicted drugs interacting with The proposed deep learning model can accelerate the discovery of new DDIs. It can support future clinical research for safer and more effective drug co-prescription.The online version contains supplementary material available at 10.1186/s12859-021-04241-1. Combination drug therapy is increasingly used to manage complex diseases such as diabetes, cancer, and cardiovascular diseases. In particular, patients with type 2 diabetes often do not only suffer from symptoms of elevated blood glucose levels but also have several comorbidities that require multifactorial pharmacotherapy. Older patients may receive 10 or more concomitant drugs to manage multiple disorders –7. DDIs . built the same DNN model but included three types of features as input: structural similarity profiles, Gene Ontology term similarity profiles, and target gene similarity profiles of known drug pairs; and used autoencoder to reduce the dimensions of each profile [Over the past decade, deep learning has achieved remarkable success in a number of research areas . Because profile . Rohani  profile . Compare profile –17. Kari profile . Zheng e profile .In Euclidean space, every pixel in an image can be regarded as a vertex in a graph, and each vertex is connected with a fixed number of adjacent pixel points. Convolutional neural network (CNN) can greatly speed up the training tasks related to images. Dhami et al. used CNN to predict DDIs directly from images of drug structures . HoweverDue to a large amount of diverse drug information data, DDI prediction in silico remains a challenge and there is still room for improvement in prediction performance. In 2010, the National Institute of Health (NIH) funded the Library of Integrated Network-based Cellular Signatures (LINCS) project. This project aims to draw a comprehensive picture of multilevel cellular responses by exposing cells to various perturbing agents . The L10Since the original drug-induced transcriptome data contains technical noise, the correlation observed between drug-induced transcriptome data and drug structure is very low. In order to reduce the impact of noise, the drug-induced transcriptome data was embedded before building a DDI prediction model. To establish a stronger relationship between the drug structure and drug-induced transcriptome data, we used both the structure information of drugs and the similarity information between drugs in the process of embedding with GCAN. As shown in Fig. To explore whether GCAN embedded features can improve DDI prediction, we compared different drug features as input in various machine learning methods –17, and To further evaluate the performance of GCAN embedded features, we examined the results of the DNN model under each DDI type. Compared with the original drug-induced transcriptome data, comparable or better classification F1-score is observed for 52 out of 80 DDI types when using GCAN embedded features, and for 41 out of 80 DDI types when using autoencoder embedded features  from Rohani and Eslahchi’s report . As comp and DS2 cabergoline and can lead to decreased adverse reaction; and there are 114 drugs that can cause an increased adverse reaction. The number of drugs that interact with the drug amodiaquine and can lead to decreased QTC prolongation or increased QTC prolongation is 12 and 96, respectively. Using GCAN embedded features plus LSTM for DDI prediction, 92.75% of the drugs that are predicted to interact with cabergoline and increase adverse reaction are most similar to one of the drugs that are known to interact with cabergoline and increase adverse reaction in the training set  was input into the trained model to predict DDIs. After excluding the existing DDIs, a total of 21,670 new DDIs were predicted. Then we used the latest version of the DrugBank database (version 5.1.7) data released in April 2020 to verify the prediction results and found that 975 new DDIs were included in the latest DrugBank database (version 5.1.7) Fig. a.Fig. 4TSulfonylurea hypoglycemic drugs are mainly metabolized by CYP2C9 of CYP450 enzyme in the human liver. It has been reported that drugs with inhibition on CYP450, such as antibacterial drugs, antidepressants, and cardiovascular drugs, can interact with sulfonylurea hypoglycemic drugs, affecting the metabolism of sulfonylurea hypoglycemic drugs, and increase the risk of hypoglycemia [sulfonylurea hypoglycemic drugs and antibacterial drugs (ATC code beginning with J01), antidepressants (ATC code beginning with N06), and cardiovascular drugs (ATC code beginning with C) can cause hypoglycemia. In addition, we also found that drugs indicated for many other diseases can interact with sulfonylureas hypoglycemic drugs to cause hypoglycemia. Online target prediction analysis [Cytochrome P450 enzyme (CYP450) is a key enzyme for drug metabolism in vivo. The inhibition of this enzyme’s activity will lead to drug accumulation and cause potential drug side effects , 35. Sulglycemia . Throughanalysis  shows thMetformin has been used in the treatment of type 2 diabetes for more than 60 years and is still a first-line hypoglycemic drug widely used in the clinic. Metformin is not easily metabolized after entering the human body, 80–100% of metformin will be discharged from the renal tubules in the form of the prototype [metformin. Studies have shown that the elimination of metformin is mediated by the transporters MATE1, MATE2K, and OCT2 in the kidney [Cimetidine, a drug for the treatment of peptic ulcers, can lead to metformin accumulation by competing with metformin for OCT2 or MATE1, which can result in a significant increase in the concentration of lactate in blood [metformin and lead to lactic acidosis  and found that the number of newly recorded DDIs is predicted by our model. On the other hand, we analyzed the potential molecular mechanisms of newly predicted DDIs of antidiabetic agents through online drug-target interaction prediction . We founWith the development of drug delivery technology, more attention has been focused on macromolecule drug , 42. Onesulfonylureas and metformin, we show that the new DDIs predicted by our model have good molecular mechanism support and many of the predicted DDIs are listed in the latest DrugBank library (version 5.1.7). These results indicate that our model has the potential to provide accurate guidance for drug usage.In this paper, we propose GCAN embedded features plus LSTM model for the prediction of DDIs on drug-induced transcriptome data. Through evaluation of different models, the proposed model is demonstrated to significantly improve the prediction performance of DDIs. With a deep analysis of drugs interacting with We used the LINCS L1000 dataset that includes ~ 205,034 gene expression profiles perturbed by more than 20,000 compounds in 71 human cell lines. LINCS L1000 is generated using Luminex L1000 technology where the expression levels of 978 landmark genes are measured by fluorescence intensity. The LINCS L1000 dataset provides five different levels of data depending on the stage of the data processing pipeline. Level 1 dataset contains raw expression values from the Luminex 1000 platform; Level 2 contains the gene expression values of 978 landmark genes after deconvolution; Level 3 provides normalized gene expression values for the landmark genes as well as imputed values for an additional ~ 12,000 genes; Level 4 contains z-scores relative to all samples or vehicle controls in the plate; Level 5 is the expression signature genes extracted by merging the z-scores of replicates. We utilized the Level 5 dataset marked as exemplar signature, which is relatively more robust, thus a reliable set of differentially expressed genes (DEGs). We took the subtraction expression values of 977 landmark genes between drug-induced transcriptome data and their untreated controls, resulting in a vector of 977 in length to represent each drug. The drug-induced transcriptome data in the PC3 cell line was used to build and evaluate the model. Data from the A375, A549, HA1E, or MCF7 cell lines were used to further validate the model. The reason we picked up data on these cells is that there are enough drug-induced transcriptome data on these cells.The reported total of 2,723,944 DDIs described in the form of sentences were downloaded from DrugBank (version 5.1.4). Drugs with more than one active ingredient, proteins, and peptidic drugs were not considered in this study, and drugs with no transcriptome data in the PC3 cell line from the L1000 dataset were also excluded. Since our model was trained and evaluated with fivefold cross-validation, adverse DDI types with less than 5 drug pairs in them were excluded. Finally, a total of 89,970 DDIs were classified into 80 DDI types and used to construct the DDI prediction model (For more information, see Additional file The DDI prediction model proposed in this study consists of two parts Fig. . First, In the GCAN graph , each noSince DDI often involves one drug causing a change in the efficacy and/or toxicity of another drug, treating two interacting drugs as sequence data may improve DDI prediction. Thus, we choose to construct an LSTM model by stacking the embedded features vectors of two drugs into a sequence as the input of LSTM. Optimization of the LSTM model in terms of the number of layers and units in each layer by using grid search, and is shown in Additional file We also perform DDI prediction using other machine learning methods including DNN, Random Forest, MLKNN, and BRkNNaClassifier. By using grid search, the DNN model is optimized in terms of the number of layers and nodes in each layer. It is shown in Additional file The model performance is evaluated by fivefold cross-validation using the following three performance metrics:n is the number of labels or DDI types. Python package scikit-learn [where TP, TN, FP, and FN indicate the true positive, true negative, false positive, and false negative, respectively, and it-learn  is used In this study, the drug structure is described with Morgan fingerprint. The Tanimoto coefficient is calculated to measure the similarity between drug structures. The transcriptome data or GCAN embedded data are all floating-points and the similarity can be calculated using the European distance as follow:d stands for characteristic dimension.where X and Y represent transcriptome data, To measure the relationship between different characteristic vectors of drugs, the Pearson correlation coefficients are calculated. First, the similarity between drugs is calculated with different characteristics of drugs. Then the similarity between drugs is used to represent drugs, and the Pearson correlation coefficients is calculated with this new characteristic as follows:E is the mathematical expectation, σ is the variance, and x and y are two different drug vectors.where Additional file 1: Fig. S1 Optimization of the LSTM model in terms of the number of layers and nodes in each layer. Fig. S2 Optimization of the DNN models with three different drug features in terms of the number of layers and nodes in each layer. Fig. S3 DDI prediction with LSTM. Table S1 Preparation of the Gold Standard DDI Dataset. Table S2 Optimal parameters of GCAN. Table S3 Performance comparison on DS1. Table S4 Performance comparison on DS2. Table S5 Performance on different orders of the drugs.


Screening Tool for Feet/Footwear-Related Influences on Fall Risk to support interprofessional health care providers in their efforts to screen for feet/footwear-related influences on fall risk among community-dwelling older adults identified at risk for falling.Screening for feet- and footwear-related influences on fall risk is an important component of multifactorial fall risk screenings, yet few evidence-based tools are available for this purpose. We developed the The study consisted of two phases. During Phase 1, results of a systematic review of lower-limb factors associated with balance and falls informed tool development. The tool's initial draft was evaluated by an external group of nine interprofessional content experts. After incorporating changes recommended by Phase 1 participants, Phase 2 was initiated. During Phase 2, eight new interprofessional experts (19.3 average years of experience) completed the three rounds of a modified Delphi study.Phase 1 experts recommended modifying eight items and rated the tool's clarity, appeal and clinical feasibility as 81.2/100, 79.1/100, and 76.1/100, respectively. Phase 2 participants suggested combining items with similar recommended actions, adding a question about orthoses, and increasing the specificity of nine items. The refinements resulted in a 20-item screening tool. Each item was approved by the Phase 2 participants with > 80% agreement after two rounds of consensus voting, reflecting the tool's high face and content validity.The new screening tool has high face and content validity and supports identification of feet- and footwear-related influences on fall risk among community-dwelling older adults. The tool can be used by interprofessional healthcare providers completing a multifactorial fall risk screening on community-dwelling adults identified as being at risk for falling. The Center for Disease Control (CDC) reported 35.6 million falls among older adults in 2018 , 9,563–4Coordinated Care Plan to Prevent Older Adult Falls (Per the American Geriatric Society and British Geriatric Society (AGS/BGS) guidelines for preventing falls in older adults , all oldlt Falls . These rlt Falls .A 2020 systematic review conducted by members of our research team identified eight factors regarding feet or footwear-related impairments or functional limitations that may increase older adults' fall risk: (1) Neuropathy and Sensory Impairment, (2) Foot Pain, (3) Foot or Ankle Orthoses, (4) Shoe or Footwear, (5) Foot Deformities, (6) Strength, (7) Range of Motion, and (8) Skin or Changes in Plantar Soft Tissues . The NevWe know of no comprehensive, clinically feasible screening tools that interprofessional healthcare providers can use to screen for feet or footwear-related impairments or functional limitations that may increase older adults' fall risk. Therefore, this study aimed to use a two-phase study design to develop a tool that compliments the STEADI algorithm and supports interprofessional health care providers in their efforts to screen for feet/footwear-related influences on fall risk among community-dwelling older adults identified at risk for falling.The study consisted of two phases. Both phases were informed by the Neville et al.  findingsThe purpose of Phase 1 (January through October 2020) of the study was to generate a strong first draft of a screening tool that reflected the eight factors regarding feet or footwear-related impairments or functional limitations that may increase older adults' fall risk that were identified by Neville et al. . During Orthoses and Shoe/Footwear categories were collapsed into one category, and the Range of Motion category was included with other items within the screening tool. Our development and review process led to an initial draft of the Screening Tool for Feet/Footwear-Related Influences on Fall Risk, a 22-item screening tool that consisted of six categories. The six categories were: Footwear and Footwear Habits, Nail or Skin Changes, Foot and Ankle Deformities, Foot and Ankle Strength, Foot Pain, and Foot Sensation. The screening tool also included recommended action steps for healthcare providers. The action steps were specific for each category and intended to support referral to healthcare providers with the expertise needed to address the problem identified. The authors of this study approved the initial screening tool and all subsequent changes.An iterative process was used to modify or eliminate questions that were double-barreled or redundant. The items were further refined to avoid double negatives, complex sentences, absolutes, or ambiguity. Because the goal was to create a screening tool that would accompany the STEADI, the members sought to format the tool like the STEADI, with a yes/no response format and “yes” answers triggering a recommended action to address the risk. To increase ease of use, the The final part of Phase 1 involved gathering anonymous feedback on the screening tool from additional interprofessional experts in the field who were not previously involved in developing the tool. To generate a list of potential experts to participate in Phase 1, the co-authors of the Neville et al.  drew upoPrior to initiating data analysis, all data were checked for completeness. Descriptive analysis to examine percent agreement and ratings for questions related to the items and the scale's overall appeal and feasibility was completed using SPSS-Version 27 . The authors revised the screening tool based on expert comments in anticipation of Phase 2.Phase 2 (October 2020 through February 2021) focused on refining the screening tool developed in Phase 1 using a modified Delphi study to evaluate the tool's content validity. A Delphi study is characterized by two or more rounds of questionnaires and controlled feedback; the specific methodologies depend on the type of technique selected . The preA new cohort of researchers and master clinicians were identified and recruited for Phase 2, using the same selection and recruitment procedures used in Phase 1. Nine of fourteen identified experts (herein referred to as “Delphi participants”) agreed to participate. This number of Delphi participants is consistent with recommendations by Trevelyan and Robinson . Delphi Delphi participants provided general feedback about the screening tool's items' clarity, quality, detail, and importance and were given the opportunity to recommend additional items. The survey for Round 1 contained 63 questions in total regarding (1) feedback on the overall format and instructions for the entire screening tool, (2) feedback on each item in the screening tool as in the pre-Delphi survey, and (3) feedback on the recommended actions presented in the screening tool with similar questions regarding clarity, detail, and importance. Round 1 results were used to refine the screening tool used for Rounds 2 and 3.Analysis methods). The survey used for Round 2 contained 19 questions.For the final scale, participants were asked to rate the importance of including each item using a five-point scale (1 [strongly disagree] to 5 [strongly agree]), and based on responses, a per-item consensus was measured  about why they did not agree. Round 3 response options were the same as round 2.The Delphi participants were instructed to apply different methodologies to rate the survey items in Round 1 than in Rounds 2 and 3. For Round 1, the Delphi participants appraised each item using three criteria: (1) clarity, (2) detail, and (3) the importance of the risk factor. For Rounds 2 through 3, they rated the items' importance of being in the final screening tool. All criteria used the same five-point scale (1 [strongly disagree] to 5 [strongly agree]). For all rounds, each question was followed by a free text box for the experts to comment about the items.a priori definition for including an item in the final screening tool was based on consensus, defined as ≥ 80% of the Delphi participants rating an item as strongly agree (5) or agree (4). While consensus for excluding was determined if > 80% of the Delphi participants rated an item as strongly disagree (1) or disagree (2)  . Contentgree (2) . For thegree (2) .Screening Tool for Feet/Footwear-Related Influences on Fall Risk were kept, as they were deemed important by the internal and external group of participants. The external group of nine experts included three podiatrists, two orthopedic surgeons, two physical therapists, an occupational therapist, and a researcher. They recommended that the screening tool's instructions be simplified by explicitly identifying how the screening tool can be used with the STEADI. The experts rated the average overall clarity of the items as 81.2 (SD = 11.7) and provided feedback on eight items. The item with the lowest clarity  was within the foot strength section and dealt with forefoot raises to assess pretibial muscle  strength. The comments highlighted the lack of clarity about the alternation of the movements and the lack of standardized tools available in the literature to assess this. The item with the second-lowest clarity rating was related to skin changes . The recommendations were to define a “poor-fitting” shoe and when skin changes should trigger a recommended action to ameliorate fall risk. The other items that needed clarifying were the foot-pain and foot-deformity items.All 22 items in the initial draft of the all patients with skin changes or flat feet are to be referred. However, only individuals first identified as being at risk for falling by the STEADI and then identified to have a feet- and footwear-related impairment are flagged for referral to appropriate healthcare providers . Therefore, the instructions on the screening tool were revised for clarification. The experts also recommended being specific about which providers should receive referrals in the recommended actions for items .Phase 1 experts also raised concern that the screening tool would result in too many positive risk factors and referrals. Part of this was due to a lack of clarity in the items; the reviewers thought that The last set of Phase 1 recommendations from the experts focused on the screening tool's layout and included recommendations on improved workflow, length, and referral suggestions. The only recommendation regarding potential items to add was related to assessing gait and the use of an assistive device. Items pertaining to assessment of gait and use of assistive devices were not added to the screening tool for two reasons. First, the tool's focus is on screening for foot and footwear-related influences on fall risk. Second, the screening tool is intended to be complimentary to the STEADI algorithm which includes directives and guidelines for gait assessment. When asked about the screening tool's appeal and feasibility, the experts rated the average appeal as 79.1 (SD = 21.0) and the feasibility as 76.1 (SD = 21.0).One of the initial nine Delphi participants did not complete all the study rounds and their data were excluded from the analysis. Because eight is still within the recommended number of participants for a modified Delphi, no additional steps were taken . The remScreening Tool for Feet/Footwear-Related Influences on Fall Risk to the STEADI distracted from the tool's primary instructions and purpose. The feedback led to the following simplified directions, “Impairments within the feet or poor footwear may be contributing to an ambulatory older adult's risk for falling. This screening tool was designed to screen for feet/footwear related influences for older adults who have been identified at risk for falling.”The Delphi participants reported that the information in the instructions describing the relationship of the The Delphi participants suggested moving the recommended actions from a third column to a row at the bottom of each category. Participants' reasoning was to provide additional space to take notes, improve the delineation of sections, and improve ease of use. They also recommended increasing consistency between items' sentence structure  and to increase the consistency of the item stems within each category .deformities of the toes,” followed by the description, “toes that are not straight or flat on the ground,” and pictures of examples, including hammertoes, claw toes, or mallet toes. It is important to note that the tool is meant for an interprofessional team and thus requires simple descriptions and directions that will be understood by all healthcare providers.The Delphi participants recommended that objective values for items such as “poorly fitted shoes” or “high arch” and definitions for words such as “callus,” “corn,” and “bunion” be added. For the strength-related screening questions, they suggested more details about the directions and potential compensations. In order to incorporate the request for additional details and maintain the simplicity of the document, a compendium document was developed that includes information about the screening tool's development process, how to use the screening tool, safety considerations, definitions, directions for scoring each item, picture examples, links to videos, and references. For example, in the section on screening for foot deformities we have included definitions for hallux valgus, flat foot, high arches, contracted digits, and examples of other foot or ankle deformities. These definitions are followed by descriptions and photos of signs healthcare providers should look for when screening for foot deformities. More specifically, for contracted digits, we provided the following definition, “Wears or been advised to wear foot or ankle brace(s) or orthoses.” These modifications led to the final 20-item screening tool.Delphi participants also suggested combining several related items  with the rationale that the treatment recommendations are similar for the subgroup of items and reducing the number of items would make the screening tool more acceptable and feasible. Additionally, the Delphi participants recommended that an item related to ankle braces or orthoses be added. This recommendation is in line with the systematic review informing the tool ; therefowalks barefoot or wears socks without shoes inside or outside the home” in the final screening tool because they believed that “barefoot (without socks) walking is much safer than wearing socks indoors” but “would endorse the question if it included wearing socks.” As the item includes wearing socks as a risk factor, the screening tool developers felt that the participant might not have read the item thoroughly; therefore, the item was maintained in the final screening tool. According to the Delphi participants, the primary reason for changing an item from neutral to agree or strongly agree was related to evidence. Our conclusion is supported by the comment provided by Delphi Participant Eight, “I had a 3 but was not familiar with specific evidence-based lit to support the idea.” See Detailed results for Rounds 2 and 3 are presented in Screening Tool for Feet/Footwear-Related Influences on Fall Risk is the first documented instrument to screen for feet or footwear-related impairments or functional limitations that influence fall risk among community-dwelling older adults. Its constructs and items reflect the eight categories of feet and footwear factors identified by Neville et al. that may increase fall risk in older adults  and subsequent targeted intervention.Screening Tool for Feet/Footwear-Related Influences on Fall Risk are further supported by James et al. , grants KL2 TR003108 (JLV) and UL1 TR003107 and UL1 TR003108 (UAMS) through the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Partial response to antidepressant medication as well as relapse and treatment resistance are common in major depressive disorder (MDD). Therefore, for most patients with MDD, there will be a need to consider changing antidepressant medication at some stage during the course of the illness. The PREDDICT study investigates the efficacy of augmenting vortioxetine with celecoxib.We describe the method used in the PREDDICT study to change participants, who were already taking antidepressant medication at the time of the screening visit, to vortioxetine. We used a cross-titration to change study participants to vortioxetine.Of a total of 122 study participants who were randomized to receive vortioxetine plus celecoxib or vortioxetine plus placebo at the study baseline visit, 82 were taking antidepressant medication (other than vortioxetine) prior to randomization. These medications were selective serotonin reuptake inhibitors, serotonin noradrenaline reuptake inhibitors, tricyclic antidepressants, mirtazapine, or agomelatine. Eighty of these 82 participants completed the changeover to vortioxetine as well as the study baseline visit. We found side effects were generally mild during this changeover period. In addition, there was a reduction in mean total Montgomery-Åsberg Depression Rating Scale score of 2.5 (SD 6.0) from study baseline to week 2 and a further reduction in mean total Montgomery-Åsberg Depression Rating Scale of 2.5 (SD 5.9) from week 2 to week 4.Changing other antidepressants to vortioxetine can be done safely and was generally well-tolerated. However, there are some antidepressant classes, in particular monoamine oxidase inhibitors that require a washout period, which were not represented in this study.Australian New Zealand Clinical Trials Registry (ANZCTR); ID number 12617000527369p; http://www.anzctr.org.au/ACTRN12617000527369p.aspx Many patients suffering from major depressive disorder (MDD) will need to consider, in conjunction with their treating doctors, changing antidepressant medication at some stage during the illness. Choosing a clinically suitable change-over strategy is crucial for achieving efficacy, reducing potential withdrawal effects from the previous antidepressant, and minimizing side effects of the new antidepressant. To our knowledge, such a strategy has not been systematically investigated for the newest available antidepressant vortioxetine under real-world conditions. Here we describe the results of a clinical trial that employed various change-over strategies for commonly used antidepressants showing that the change-over strategies to vortioxetine were safe and generally well-tolerated while achieving efficacious treatment outcomes.Major depressive disorder (MDD) is a serious problem worldwide, with chronic illness common . With th3 and 5-HT7 antagonism, partial agonist properties at 5-HT1B receptors, agonist properties at 5-HT1A receptors, and potent inhibition of the serotonin transporter  present the cross-titration method used in PREDDICT; (2) describe side effects from vortioxetine, with those participants on no antidepressant at time of screening as a comparison group; and (3) compare time to onset of efficacy of vortioxetine between those participants cross-titrating from a previous antidepressant vs those with no antidepressant at screening.The PREDDICT study is an 8-week randomized controlled trial (RCT) plus 6-month post-RCT follow-up period investigating the short-term efficacy of augmenting vortioxetine with celecoxib in MDD  as well Study participants who were taking a different antidepressant than vortioxetine participated in a “pre-baseline” visit, where advice was given by the trial psychiatrist on the cross-titration process. For study participants taking SSRIs, participants first halved the dose of the SSRI and then commenced vortioxetine at 5 mg daily on the same day. After a further 3 days, the SSRI dose was halved again. This was continued  until the SSRI was at a dose where it could be ceased , once a study participant had halved the dose, vortioxetine was commenced at 5 mg daily. After 3–4 days, the dose of the SNRI was halved again. This continued until the study participant was taking the lowest possible daily dose of the SNRI  before it was ceased in the next step. To try to minimize discontinuation symptoms, when study participants were on the lowest dose of an SNRI , the SNRI was taken on alternate days for 3–4 days before ceasing. Vortioxetine continued at 5 mg daily during this period, and once the SNRI was ceased, study participants attended for the baseline visit. There were some exceptions to this, in particular for study participants taking venlafaxine XR 300 mg daily, where a longer time frame was used to reduce to one-half of the initial antidepressant dose (study participants remained on venlafaxine XR 225 mg daily for at least 3–4 days before decreasing any further). With initial higher doses of SNRIs, study participants did not commence vortioxetine 5 mg daily until the SNRI was reduced by at least 50% of the initial dose.Tricyclic antidepressants and mirtazapine were changed over in a similar way to the SSRIs. If a study participant was taking a low dose of mirtazapine and this was needed to assist with sleep, then this could be continued during the study. Monoamine oxidase inhibitors (MAOIs) such as phenelzine and tranylcypromine require a washout period of 14 days ; howeverAgomelatine could be ceased without tapering. Therefore, study participants taking agomelatine (without another antidepressant) had the last dose of agomelatine the night before the baseline visit. Vortioxetine was then commenced the next day at the study baseline visit.For those study participants taking 2 antidepressant medications prior to the pre-baseline visit, if 1 of these antidepressants was agomelatine, first this was ceased without tapering as described above. Then the other antidepressant medication was cross-titrated with vortioxetine in the same way as described above for the particular antidepressant class. For the other participants taking 2 antidepressants prior to the pre-baseline visit, 1 of these antidepressants was mirtazapine. Therefore, the other antidepressant was tapered and ceased first, then mirtazapine was reduced to a lower dose, and then vortioxetine 5 mg daily was commenced.Participants were asked at the baseline visit about any side effects and antidepressant discontinuation symptoms they had experienced during the cross-titration period. We defined discontinuation symptoms as those described in the literature such as dizziness, shock-like sensations, or nausea  that werAt the baseline visit, study participants randomized to celecoxib received vortioxetine 5 mg daily. If the study medication was well tolerated, vortioxetine could then be increased to 10 mg daily at the RCT week 2 visit, with potential to increase to vortioxetine 20 mg daily at the RCT week 6 visit. Vortioxetine was not increased further than 10 mg daily while celecoxib was co-administered, as it is advised to halve the dose of vortioxetine during co-administration with CYP2D6 inhibitors . Those sAt all study visits after the baseline, participants were asked about side effects, including the emergence of new side effects, and were asked to rate each side effect as mild, moderate, or severe. Additional information such as the frequency of the side effect could also be written in the severity or duration column of the side effect scale  at the time of the PREDDICT screening visit. One study participant had commenced vortioxetine several months before the study baseline visit and was therefore not included in the cross-titration or comparison (no antidepressant at screening) group. One study participant had been randomized (had completed the changeover of medication to vortioxetine) but did not proceed with the study baseline visit due to a further lowering of mood. Another study participant had been randomized but advised the study team on the day of the baseline visit they had not commenced the changeover of medication to vortioxetine and did not wish to proceed. A further study participant commenced the changeover of antidepressant to vortioxetine but at day 4 of the 8 day cross-titration ceased vortioxetine and returned to the previous dose of antidepressant due to lowering of mood . Therefore, a total of 80 study participants completed the cross-titration of antidepressant medication plus the study baseline visit.Six participants were taking 2 antidepressants at the time of the pre-baseline visit. Of these 6 study participants, 4 were taking agomelatine plus another antidepressant (desvenlafaxine or duloxetine). One study participant was taking a combination of venlafaxine XR and mirtazapine at the study pre-baseline visit, while another was taking a combination of citalopram and mirtazapine.The majority (61.3%) of study participants who were changing antidepressant medication to vortioxetine (and then went on to complete the study baseline visit) were female . Study pThe cross-titration period ranged from 1 day to 21 days, with study participants who were taking agomelatine alone not requiring a cross-titration . The longest changeover period was for venlafaxine XR, with doses of 300 mg daily requiring a 21-day cross-titration.Except for nausea, there were more study participants reporting each of the side effects listed in P = .047 at RCT 2) as were rates of abdominal pain, headaches (P = .016 at RCT 2), and chest pain or discomfort. A greater percentage of participants on no antidepressant medication at screening reported nausea up to and including the RCT week 2 visit (P = .031); however, this group reported lower rates of nausea at the RCT week 4 visit (nausea reported by only 1 participant at RCT 4). Rates of diarrhea, agitation, and sweats or flushes were higher in the group of participants who cross-titrated antidepressant medication to change to vortioxetine. In both groups, a similar proportion of study participants reported pruritis at the RCT week 2 visit .As a comparison group, rates of side effects in those participants not taking antidepressant medication at the time of screening are in From the criteria we described in the Methods section to define discontinuation symptoms  , 19 studFor 3 participants who experienced discontinuation symptoms, these symptoms were still present at the RCT week 2 visit . HoweverP = .11) or RCT week 4 visit (P = .97).From the baseline visit to the RCT week 2 visit, we observed a mean reduction in total MADRS score of 2.5 (SD 6.0) in those study participants who had undergone a cross-titration from a previous antidepressant compared with a mean reduction in total MADRS of 4.5 (SD 6.4) in those participants who were not taking antidepressant medication at screening. From the RCT week 2 visit to RCT week 4, there was a further decrease in mean total MADRS for the group who had cross-titrated to vortioxetine of 2.5 (SD 5.9) and a mean decrease in total MADRS of 2.3 (SD 5.9) for those participants without antidepressant medication at screening. There was no significant difference in the change in MADRS between the 2 groups at the RCT week 2 visit  were taking an antidepressant medication prior to commencing the PREDDICT study. We used a cross-titration method to change study participants from their current antidepressant medication to vortioxetine. This method was generally well-tolerated; however, some study participants experienced a significant lowering of mood during this changeover period, with 2 study participants commencing the changeover to vortioxetine but not going ahead with the study baseline visit.Side effects from vortioxetine were generally mild (as reported by study participants), with the most common side effects being nausea, diarrhea, and mild headaches in the group who cross-titrated antidepressant medication. By comparison, in the group of participants who were not taking antidepressant medication at time of screening, the most common side effects were nausea, headaches, and tiredness. In total, 36 of 118 study participants (30.5%) experienced nausea. This rate of nausea was within the range reported in previous clinical trials of vortioxetine, with rates of nausea of 8.8% to 38% reported in short-term trials and 8.8% to 31% reported in long-term trials . In our Withdrawal or discontinuation symptoms from the antidepressant that the study participants were changing from were also generally mild and short-lived. We found this appeared to be helped by commencing a low dose of vortioxetine (5 mg daily) so that study participants did not have a washout period. However, if there had been study participants commencing the changeover to vortioxetine from a MAOI, we would have had to use a washout period of 14 days .We could also have used a washout period; however, as study participants were already experiencing significant depressive symptoms at the time of screening, we chose the cross-titration method. A randomized double-blind study switched adults with MDD and an inadequate response to an SSRI or SNRI to vortioxetine or agomelatine . InvestiA reduction in mean total MADRS score from baseline to RCT week 2 and a further decrease from RCT week 2 to RCT week 4 were observed in the group who cross-titrated to vortioxetine as well as the group of study participants not taking an antidepressant at screening. Study participants already taking antidepressant medication at the screening visit could possibly be seen as treatment resistant, which may affect treatment outcome. Although in the first 2 weeks of the RCT this lowering of MADRS appeared to be greater in the group not taking an antidepressant at screening, this difference was not statistically significant. The subsequent decrease in mean MADRS from RCT week 2 to the RCT week 4 visit was similar in both groups. We only reported the change in MADRS up to and including the RCT week 4 visit, as any delay in onset of efficacy of vortioxetine should have emerged by this time.Our method of cross-titration has some limitations. As stated previously, it cannot be used for individuals who are changing from a MAOI to vortioxetine. There were also small numbers of study participants changing from some antidepressant medications  to vortioxetine.Changing from another antidepressant to vortioxetine can be done safely while achieving treatment efficacy with vortioxetine. The cross-titration method we used to change study participants from different classes of antidepressants  was generally well-tolerated.pyaa092_suppl_Supplementary_TableClick here for additional data file.


The approach and understanding developed represents a new roadmap for design of supramolecular drugs to target RNA structural motifs across biology and nucleic acid nanoscience.RNA targeting is an exciting frontier for drug design. Intriguing targets include functional RNA structures in structurally-conserved untranslated regions (UTRs) of many lethal viruses. However, computational docking screens, valuable in protein structure targeting, fail for inherently flexible RNA. Herein we harness MD simulations with Markov state modeling to enable nanosize metallo-supramolecular cylinders to explore the dynamic RNA conformational landscape of HIV-1 TAR untranslated region RNA (representative for many viruses) replicating experimental observations. These cylinders are exciting as they have unprecedented nucleic acid binding and are the first supramolecular helicates shown to have anti-viral activity  MD simulations and Markov state modeling explore induced fit binding of metallo-helicates to bulges in dynamic TAR RNA, reproduce experimental data, show how RNA conformational flexibility is reduced, and give mechanistic insight into insertion. Infectious disease represents one of the greatest current threats to humans as demonstrated by the frequency of recent lethal viral outbreaks: 4 out of the 10 greatest threats identified by the World Health Organization are viral related. While vaccines offer long-term eradication or suppression, they are bespoke to the disease and their development and implementation across a global population is slow. There is therefore a pressing need for a new generation of drugs that could hold an emerging disease at bay while bespoke solutions are created; broad-acting anti-viral agents having different molecular designs and molecular targets, offering a diverse platform that maximizes the potential preventative effect against new diseases.1–5  of the infectious agents are of particular interest with RNA increasingly recognized as a druggable target.6,7 The rapid emergence of infections, and subsequent rapid evolution of viral genetic sequences, means that drugs that target a specific sequence are unsuitable. However, agents that target a specific nucleic-acid structure could be much more interesting. In particular, the untranslated regions (UTR) at both 3′ and 5′ ends of many viral genomes are not only highly structured but often share common structural elements7–10 that are functionally essential and so conserved as the virus evolves (drifts) genetically.10,11 Indeed, structure-affecting mutations in the UTR have been used to create live attenuated or inactivated vaccine strains.12,13 UTRs have been mostly studied in RNA viruses, such as HIVs,7,10,14,15 coronaviruses,16–18 dengue,11,12,19,20 zika21 and other flaviviruses22 and, in every studied case, functional involvement of the UTR has been shown in either initiation of replication16,19 (by recruiting proteins or by direct interaction with the ribosome) or regulation of the replication cycle. The most studied example is the retrovirus HIV-1 which contains a bulge in the first stem loop of the 5′ UTR of its RNA genome,23–30 the structure and dynamics of which are crucial for initiation of viral replication. Similar bulges are found in UTRs of other RNA viruses including coronaviruses and SARS-COV-2. These UTR structures represent exciting potential anti-viral targets.Modern drug research tends to focus primarily on the protein targets as the effectors of disease. However, to target broad classes of disease, drugs that target the nucleic acids4,31–34 The molecular structural information needed for such recognition is not yet available for most viruses, and crystal structures of drugs bound to RNA structures are rare (and not necessarily representative); new molecular-level understanding of such binding is a critical need. Structural studies on RNA are further complicated by the inherent flexibility of RNA molecules, which requires an understanding of their dynamics not just their ground state conformation. Consequently, simple molecular docking will not suffice; by contrast molecular dynamics potentially allows the energy landscape and structural flexibility to be probed. Herein we employ molecular dynamics to explore in detail, for the first time, a nano-scale drug inserting into a bulge in a UTR viral RNA, replicating experimental observations and gaining fundamental new insight into the dynamics of the RNA and of the drug entry process; crucial intelligence to inform design of new UTR-structure-targeting drugs. The nano-scale drugs studied are supramolecular cylinders, which not only have unprecedented RNA bulge-binding ability but are the first in class of metallo-supramolecular architectures to show potent anti-viral activity in cellular assays.35 There is a growing interest in the application of metallo-supramolecular architectures in biology.36–41Structure-based recognition of RNA (and DNA) by drugs is still very much in its infancy.42,43 It is also unique in threading through an RNA cavity, interacting with all of the internal structure. These cylinders also bind bulge structures in RNA, prevent TAT protein from recognizing the binding site in the TAR sequence of HIV35,44 and arrest HIV replication in mammalian cells.35 The strong evidence of binding and in-cell efficacy, makes this an ideal test-bed to investigate whether molecular dynamics simulations can identify the processes that underpin the kinetics of targeting highly flexible RNA strands. At the same time, it provides a suitable challenging size of drug, and one with large, nanoscale, 3-dimensional molecular surfaces whose match and strong binding to the 3D shape of RNA structural motifs should collapse the RNA's conformational landscape to a non-functional (impotent) state. The cylinder exists in two enantiomeric forms, both of which bind RNA bulges. Experimental X-ray crystal structures are also available for unbound cylinders;45,46 the calculated DFT structures herein are almost identical.As a suitable UTR structure for our studies we chose HIV-1 TAR RNA which is both experimentally well described and representative of wider viral UTR structural motifs. As a drug we chose a nanoscale metallo-supramolecular cylinder because it is unique as a nano-drug that has previously been crystallographically characterised when bound within an RNA cavity (a perfect three-way junction (3WJ)) .42,43 It47–52 been found to be unsatisfactory – over such timescales they induced structures not seen experimentally. With longer simulations being available, the conformational space sampled can deviate further from the absolute minimum energy point and explore the importance of non-covalent interaction dynamics as pi-stacking and hydrogen bonding.53–57 However new forcefields50–52 have become available and we show now that the Rochester-Mathews forcefield51,53 can be used to simulate RNA over long timescales, reproducibly, not only for free RNA but for drug–bound complexes. The Rochester-Mathews forcefield is publicly available giving it the potential to be accessed and implemented by all. It uses the same underpinning level of DFT theory as that applied to metal-containing cylinder coordination compounds creating an overall consistency. Moreover, there are ways to accurately model NMR ensembles of RNA structures without the need of extensive MD simulations.58 Collectively we accumulated over 200 μs of simulated time; such long and data-rich simulations on a flexible RNA system, brought new challenges in analysis. We address these by applying Markov state modeling59,60 to the problem and show that this enables us to identify stable and metastable conformations among the millions of frames.For multi-microsecond simulations, classical MD forcefields describing the dynamics of both RNA and DNA have until very recently60 and Markov State Modelling (MSM). This involves reducing the dimensionality by choosing appropriate features of the simulation and identifying macrostates of each simulations using MSM and extracting those metastable structures with Perron-cluster cluster analysis (PCCA). Those extracted structures and the whole simulation are also presented in the Leontis–Westholf61 nomenclature using Barnaba.62 A detailed explanation of this workflow is included in ESI.‡Overall we have performed 123 simulations of at least 1 μs and up to 10 μs, overall ∼200 μs including several shorter runs with varying initial conditions. To analyse the vast volume of data, over 200 000 000 coordinate frames, we employ the PyEmma workflow50–52 to conserve structural features of viral stem-loop RNAs , and to establish the effectiveness of our approach to analysis, we first explored the dynamics of poliovirus stem loop (pdb: 2GRW)63 coxsackievirus stem loop (pdb: 1RFR)64 and HIV2-TAR (pdb: 1AJU)65 RNA with no bound drugs. The simulations reliably reproduced NMR observations for the stem loops (including regions of non-Watson–Crick pairing) and the predicted effect of a small bound ligand on the HIV2 TAR. Indeed for poliovirus stem-loop, the MD simulations reveal and explain features that are observed in the NMR structural data, but have not previously been satisfactorily captured in the deposited conformations, and for HIV2-TAR shows how the ligand-free RNA structure deviates from the conformation of the bound state, demonstrating the effect a binding molecule can have on an RNA structure: a detailed analysis of these free RNA simulations is included in ESI.‡To confirm the ability of the forcefield1ANR) structures. In an effort to avoid introducing biases and acceleration methods to the simulation we chose to explore the conformation landscape by starting simulations from different local minima as described in the original HIV-1 TAR NMR solution structure.66 There are 20 NMR solutions proposed and we started from five such minima . For each of these higher energy solutions a 2 μs simulation retained the characteristics consistent with the NMR structure and did not deviate into unnatural (loosely bound) conformations. From each starting point similar features can be observed as the simulation proceeds which indicates that the forcefield can reproduce transitions within the landscape of a few μs per solution. These unbiased MD simulations capture the conformational changes of the RNA across the energy landscape for the first time, and clearly reveal the variation possible in the RNA structure and the range of conformations sampled (and which a drug could encounter and sample). Importantly, time-lagged independent component analysis (TICA) of the trajectories  creating a transient folding up of the second stem. Such a folding was not observed in the HIV-2 TAR simulation.cis or trans WC/Hoogsteen to A35.The loop region is characterised by limited stacking between bases and common WC pairing between C30 and G34. Transient non-WC pairing can include C30 Examining the runs starting from the different local energy minima; the first simulation starting from 1anr1 identified 3 distinct states, that can be recognised even by the PCA analysis. All are energetically and conformationally close together as seen by the RMSD and ERMSD. PCCA analysis shows one to be in much higher occupancy, clearly the ground state. A second simulation also starting from 1anr1 sampled a wider conformational space. Base pairing of stems was retained although stacking between C19:G43 and A20:U21 was not, although it is observed in the NMR. After that, stacking does continue all the way to the loop. At the loop a few different conformations were sampled that mostly gave rise to the different MSM states identified. C30 base pairs with either G33 or G34. In the bulge region U40 is stacked strongly with C39 but not always to C41 and transient, short lived pairing takes place between all bulge residues and either of C39 and U40, with pairing types including both sugar and Hoogsteen edges as well as in the trans position. MSM analysis gave 6 different states.The simulation starting from the third NMR solution, 1anr3, yielded 5 local minima in the TiCA projections and CK test allowed for 5 states in MSM analysis. Overall, stacking and pairing throughout the stems is conserved and transient pairing within the bulge region is similar to that of the previous run. Most importantly the second state is very reminiscent of the ground state.et al.67 In that work, 3 nucleotide bulges are observed to adopt multiple conformations; we replicate these multiple conformations in our simulations  has been discussed by Orlovsky The seventh, 1anr7, and twelfth, 1anr12, structures are quite different from the ground state and this brings challenges for the simulation: specifically, the loss of A helix structures which is characterised by the overall elongation of G17 to G33 distance can be testing to any forcefield. Nevertheless, starting from 1anr7, the stacking and pairing remains consistent. PCA identified 2 states whereas TiCA suggested 6 states and the CK test is also passed with 6 states. The first 4 states are reminiscent of the ground state with different loop configurations, namely sugar to Hoogsteen between C30 and A35, or less often trans WC to Hoogsteen. In the other two states, U25, which generally points outside the bulge can create temporary long-range interactions with loop residue G33.Starting from 1anr12, which is also very elongated with a sharp backbone kink in the bulge area, also retrieved most of the properties of the ground state. Pairing and stacking remain consistent for the stems. In the loop the common C30 to G34 pairing is stable along with a transient Hoogsteen to sugar between A35 and C30. In the bulge region stacking between C39 and U40 is strong and most of the transient non traditional base pairings are also seen. PCA revealed 2 states whereas TiCA revealed 5.The results demonstrate that the forcefield can satisfactorily retain characteristics of the structure as described by the NMR experimental constrains.1anr structure, there are some TAR RNA structures with various different bound drugs, and so for comparison we also explored as a starting point one such structure 68 from which we had removed the drug. The structure, after removing the ligand, has some differences with the 1anr structure: pairing on the stems is the same, but stacking is disturbed before the bulge, probably since U23 is WC paired with A27.In addition to the unbound 52 behaved well for every case of RNA molecular dynamics, even in cases outside the ground state of the structure in question.When using this as the starting point for a 2 μs simulation, the loop folded back onto the bulge (from which the ligand had been removed) forming interactions from U23 and C24 to A35, and the stem remained folded for much of the simulation. The bulge stacking did not return to the transient pairings seen in the earlier simulations. PCA analysis of the simulation revealed 3 states and TiCA 6, which was also passed the CK test on with MSM with the sixth state being ground state of this run. The simulation demonstrates how ligand binding can modify the structure and dynamics of the TAR RNA and again highlights that docking, while a useful guide, may miss key features and opportunities. The Rochester forcefield3,52 We initially undertook simple docking calculations as described in methods using all 20 structures from pdb; 1anr TAR RNA NMR study. The results are dominated by different forms of bulge region binding. While the two enantiomers do show slightly different binding energies, the Autodock Vina69 as other docking software  as other docking software tends to underestimate the electrostatic contribution when a charged molecule is involved. Nevertheless the docking scores are high compared to other small molecule drugs assessed by this method reflecting the larger available surface of the cylinder.Disney has recently used docking to screen libraries of small molecules binding to RNAs including TAR.It is interesting to compare the results of docking with overall results of subsequent MD simulations. In particular in the MD simulations, capping of the open terminal bases is a transient, but relatively stable (more than 2 μs) location seen with both enantiomers. Although only a local minimum in the interaction of cylinders with TAR it highlights the limitations of docking in targeting nucleic acids because, across all 20 NMR solutions of TAR RNA, the terminal bases are coplanar only in one (the ninth). Consequently only in this structure solution does the docking reveal the end capping as a potential binding site. So docking outcomes are constrained by the rigid RNA structure(s) used in the docking, whereas in reality – as we shall see – RNAs are highly fluxional and dynamic molecules that access much structural space. Thus while such simple docking studies are valuable for high throughput screening they might be more suited to small molecules where the molecule is less likely to have a major effect on RNA conformation. For the larger cylinders the size of the binding surface means that induced conformational change is more likely and so more sophisticated MD can offer greater insight into the interaction. Crucially, while the docking showed bulge region binding, bulge insertion by the cylinder was not observed.1ANR solutions examined earlier.To examine the interaction between TAR and the cylinders, simulations (112) started with the cylinder  in the simulations' timescale. Consequently a single simulation would fail to explore all binding sites and conformations. Instead we take the quite different approach of using multiple simulations (1–10 μs) from different starting points which allows the cylinder to explore a much greater range of RNA conformations and to encounter multiple potential binding sites. By combining this with Markov state modelling analysis we are now able to explore effectively the dynamic conformational landscape of the TAR RNA – cylinder complex.en route, of which some represent local minima with longer residence times (though still transient) and are identified from the MSM analysis  reproduces this binding as captured by the crystal structure we extracted the G17:C45 bases and the cylinder from a frame of the longest lived position and we then optimised that structure at the ssb-d-D3/LANL2DZ ) and superimposed it on the binding mode extracted from a crystal structure. The overlap (Often the cylinder (both enantiomers) found a local minimum, which it occupied for at least 1 μs at a time, and in which it capped the terminal G17:C45 bases . Some RN overlap  is extreM enantiomer on average is longer than for the P implying that the M enantiomer may have a higher affinity for the grooves although the kinetics were not adequately sampled to quantify difference.The cylinder is commonly observed exploring the RNA grooves, primarily the groove of the first stem. The residence time for the The cylinder can take advantage of unpaired open bases of the loop and interact transiently there , but this is less commonly observed in the simulation compared to other locations. Loops are a common feature in RNA structures  but seem not to be a particular target for the cylinder, consistent with our experimental observations.35,44), with M and P being very similar in their preference for this location. RNA conformations that involve the loop bridging to the bulge (U25) can be stabilised for longer (compared to free TAR), with the cylinder sitting on top of the bridge or mediating stacking. In the absence of the bridge, the cylinder can also sit between the bulge and the opposite RNA strand, in a position in which it opens up the base pairing protecting the TAT binding site. In the case that the cylinder sits on the bulge nucleotides, it stabilises the transient base pairing and dislocates the counter ions that would normally reside there which leads to an overall elongated structure of the RNA with minimal helicity.The cylinder is most frequently found around or on the bulge  in the s72,73 Given that the bulge-area is the most frequent location for the cylinder prior to bulge-insertion, it seems likely that this region could also be a preferred area of binding for other dinuclear complexes that cannot insert inside the bulge; for example differently shaped metallo-helices have been reported to not remain bound to TAR in electrophoresis,74 in contrast to the bulge-inserting cylinders herein,35,44 and might be more loosely associated outside the bulge.In this context it is noteworthy that Keene and Collins have explored the binding of a dinuclear ruthenium polypyridyl agent (but of quite different shape to the cylinders) to a TAR-like RNA and proposed that it might bind around the groove near the bulge.M and P enantiomers, insertion into the bulge is observed; once in the bulge the cylinder is strongly bound and remains there. In this unique binding mode, the cylinder sits in a V-shaped cleft  and C39–G26. The cylinder starts to slide around placing its pyridyls into the bulge; these pyridyls initially encounter the sugar of U23. U25 and C24 swing back and forth with U25 also encountering the pyridyls and transiently stacking with pyridyls as does A22. The crucial point of insertion involves the cylinder stacked with G26–C39, twisting around and inserting through the centre of the bulge . It doesThe MSM analysis of this entry process shows just two principal states; once the cylinder has moved from its location just outside and starts to open and enter the bulge, the energy landscape drops rapidly down into the final position where the cylinder is fully inserted and where it remains .P enantiomer from the same starting position ]2+, which doesn't intercalate into duplex RNA, can bind at RNA mismatch sites,75 where it is proposed to do so by insertion, with extrusion of the mispaired bases. The organic intercalator ethidium has been proposed to bind one-base bulges in RNA,76 and metal complexes bearing a ‘phi’ intercalator suggested to bind near the TAR bulge from cleavage experiments, though that is not yet well understood at a structural level.77–80 This insertion of a three-dimensional structure represents a unique and exciting approach to target RNA structures.This bulge insertion is a fascinating illustration of how a three dimensional nano-size agent might target the interior of an RNA structural feature, not by hydrogen bonding to the bases but rather by using its external pi-surfaces to recognize the surfaces inside the structure. To that extent the structure resembles a three-dimensional version of intercalation, and in that context it is notable that Barton has shown that the ‘light-switch’ intercalator [Ru(bpy)81,82The simulations suggest that the binding interaction between the cylinder and the TAR-RNA should be characterised as an “induced fit” interaction, meaning that the cylinder does not recognise the bulge cavity in the traditional lock-key manner but rather it induces the precise conformation of the RNA. This complicates the free energy landscape estimation. Although we do get an idea of the landscape using TiCA and PCA we do not believe that the space is sufficiently sampled and therefore MSM probabilities only reflect the sampled space. Mmpbsa techniques cannot be used as removing the cylinder from the final complex exposes a large hydrophobic cavity and an RNA structure that is not in a minimum. Therefore in this paper we have focused on the kinetics and mechanics of the binding process and not on the free energy estimation of the binding. However, in other systems, metadynamics and transition path sampling (TPS) have previously been applied to study the interaction of metal complexes with nucleic acids and proteins.84 becke97-d,85 and TPSSh86 with D3 dispersion correction87 for the first two and D3BJ for the last with of Def2-SVP basis set. The optimisation was performed under tight driver criteria and increased grid to xfine settings for convergence. Partial charge distribution on atomic positions was calculated with the ESP module under overall restrain of charge. Visualisation of the charge distribution at the surface was done in VMD 1.9.2 . Exhaustiveness was set to 1000. Additional docking to just the terminal bases, specifying the docking box to the first 3 base pairs showed that only 2 out of the 20 solutions allowed for capping-mode docking.Autodock vina89 that generated parameters for the metal centres at the wB97XD9/6-31G*86 level of theory using Gaussian09 (ref. http://parmed.github.io/ParmEd/html/index.html).Parametrisation of supramolecular cylinder: already DFT optimised geometries of the cylinders were split into 5 residues  that were fed to MCPB.pyn09 ref.  as well 66 was achieved with pdb2gmx program of GROMACS 2019.2  were analysed to remove periodic boundary conditions and rotations using gromacs' trjconv program. After removing the water the trajectories were analysed with pyemma2.5.6 and pyemma 2.5.7,We also explored simulations for the ruthenium cylinder  in place of the iron cylinder. The ruthenium cylinder behaved analogously in its binding, though its movement was slower due to the increased molecular mass.60 The workflow involves principal component analysis, time dependent component analysis, and Markov state modeling and Perron cluster cluster analysis.To analyse the simulations and identify different micro-states on the energy landscape of each run, we followed the Pyemma workflow.To identify the best features to apply the workflow to, we explored a variety of potential different features to see which best captured the kinetic variance that occurred during the simulations:1. Position of centre of mass (COM) of each residue is a low dimensional and relatively efficient way to capture different states, including simulations that involve one or more cylinders.2. Taking advantage of the fact that each residue has an atom named N3, which is away from the backbone, we created a matrix of distances between these N3 atoms, which although high in dimensionality captures nearly all the kinetic variance. For the cylinder simulations, we also added the distances of the metal ions (Fe or Ru) and the resulting matrix can capture adequately the kinetics of the system during the simulation.3. The distances between the phosphorus atoms in the backbone.Of these approaches 2 proved the most useful and was applied to all the simulations.http://www.emma-project.org/latest/index.html. Lag times for MSM model were chosen from the convergence at timescales of identified processes. Only models that used all of the states and could pass the Chapman–Kolmogorov test were continued to Perron-cluster cluster analysis (PCCA)which led to extraction of states with certain probability and structure in pdb format. Not all simulations were long enough to produce an appropriate Markov state model, and it should be noted that the Markov state models as used here are meant to describe or sum up the particular simulations and not the whole system.For each simulation, Principal Component Analysis (PCA) was carried and the projections between the first 4 PCs are plotted, followed by time-lagged independent component analysis (TICA) for lag times 1 to 5000 steps. The lag time for which the fewer number of TICA dimensions were necessary to capture 95% of the kinetic variance was chosen for further analysis. The number of clusters was chosen by examining the convergence with regards to VAMP2 as described the original paper and 62 all long production molecular dynamics runs, as well as states identified by PCCA, were analysed using barnaba resulting in 2D Leontis/Westhof classification61 of base interactions as well as E-RMSD as defined by barnaba software, RMSD and J-couplings.The extracted state and the full length of the simulation were analysed with Barnaba:This study provides an unprecedented platform to inform design of agents that target different important RNA structural motifs found in nucleic acid nanoscience and biology, such as this bulge cavity present in the UTR of many different viruses. We show that MD simulations, in conjunction with Markov state modeling, allow the dynamic conformational landscape of RNA to be probed and thus different and more relevant binding modes and capabilities of a potential drug to be identified; by contrast, docking to rigid RNA structures is not sufficient to guide such drug designs. The simulations provide crucial new information, not readily accessible by experiment: they show insertion of the cylinders into the cavity of the RNA bulge in a similar binding to that seen for RNA 3-way junctions; they not only provide insight into the ultimate bound structure but also its wider effect on RNA conformation reducing the RNA conformational flexibility once the cavity is bound; and, for the first time, they provide insight about the molecular mechanism through which a drug might enter a cavity in the RNA UTR, involving stacking on and sliding down bases and base pairs. Together these new molecular insights and the combined modelling and analysis approaches that have enabled them and can be more widely applied, will transform understanding of how to create supramolecular drugs that insert effectively into RNA cavities and can guide new designs against a spectrum of critical RNA viruses that threaten human well-being.DFT calculations, MD simulations and MSM analyses were designed and undertaken by LM. MJH conceived the project, which was supervised by MJH and IS. All authors analysed the data and discussed the results, and MJH and LM drafted the paper. All authors commented on the manuscript and contributed to the final version of the manuscript.There are no conflicts to declare.SC-012-D1SC00933H-s001SC-012-D1SC00933H-s002SC-012-D1SC00933H-s003SC-012-D1SC00933H-s004SC-012-D1SC00933H-s005