Methods: Breast (n = 10) and prostate (n = 10) cancer survivors will participate in a 24-week feasibility study, including (1) 12 weeks of one-on-one virtual personal training with an exercise physiologist (EP) via Zoom, and (2) individual exercise for a 12-week follow-up period using recordings of Zoom sessions for guidance. Physical assessments and surveys will be implemented at baseline, 12 weeks, and at the end of the study (24 weeks from baseline). Conclusions: While virtual exercise programming became popularized during the pandemic, evidence is still required to understand whether it can successfully address barriers and promote participation.Background: Despite the known benefits of physical activity in cancer survivors, adherence to exercise guidelines remains low. Known barriers to adhering to guidelines include a lack of time and an unwillingness to return to treatment facilities. Virtual exercise programming could assist in mitigating these barriers. This protocol presents a single arm pilot study exploring the feasibility of personalized Zoom-delivered exercise training for breast and prostate cancer survivors. A secondary objective is to determine the preliminary efficacy of participation on body composition, estimated VO Exercise is safe and beneficial for cancer survivors , and is Researchers have sought to identify exercise preferences among survivors to ameliorate low adherence to exercise guidelines . EvidencThe purpose of this study is to assess the delivery of live virtual personal training exercise sessions among breast and prostate cancer survivors. Breast and prostate cancer survivors were chosen for optimal representation of female and male participants. Breast and prostate cancer are the most common female and male cancers, respectively . Further2max, resting heart rate, and resting blood pressure; and indicators of physical activity behavior, which will be measured through survey items assessing exercise self-efficacy and intentions to remain active. An exploratory qualitative objective will focus on understanding the best practices for quality virtual exercise program delivery through exit interviews.The primary endpoint is to assess adherence to intervention delivery by measuring attendance. Attendance is defined as frequency of Zoom sessions attended and completed over the 12-week study period. Secondary outcomes include retention, defined as participation from the baseline through to the final assessment; enrollment, defined as the percentage of patients approached who sign consent; Zoom session sound and visual clarity will be measured through end of session survey questions; changes in body composition will be measured by a bioelectrical impedance analysis, hand grip strength, one repetition maximum leg press, estimated VO2max and vitals, and surveys assessing indicators of physical activity behavior. Participation will also include an exit interview at the end of the study. Each patient’s active participation will last approximately 6 months. The study timeline is described in This single-arm pilot study will use a pre- and post-test study design. In total, 20 participants will be recruited, 10 female breast cancer survivors and 10 male prostate cancer survivors. Participants will train with an exercise physiologist (EP) for 12 weeks, 3 times per week for 50 min via Zoom (Part 1). For the following 12 weeks (Part 2), participants will be instructed to complete self-directed exercise training guided by 15 recordings from their supervised Zoom sessions chosen by the EP and shared via a link to HIPAA-compliant Box cloud folders. Assessments will take place at baseline, at the end of Part 1 (approximately 12 weeks after baseline/mid-study), and at the end of Part 2 (approximately 24 weeks after starting intervention/end of study). Assessments will include body composition, strength, estimated VOParticipants will be recruited in clinics by participating health care providers  or by the study-assigned clinical research coordinator . Assessments to determine eligibility for this study will be completed only after obtaining informed consent. All screening procedures will be performed within 30 days prior to enrollment. If referred by a health care provider, the clinical research coordinator will call the patient and obtain verification of the potential subject’s identity by collecting at least two Electronic Health Record (EHR) identifiers . After receiving this information, the clinical research coordinator will conduct a review of comorbidities and oncologic medical history via chart abstraction.Inclusion and exclusion criteria is determined through electronic health records. Full inclusion and exclusion criteria are highlighted in t-test at the 0.05 significance level. In estimating percentages of retention and consent, the maximum width of the 95% confidence interval would be 0.46.Twenty participants will be recruited. This study was designed to primarily assess the number of Zoom sessions that subjects participate in, with a goal to achieve a minimum of 70% of exercise sessions (26 out of 36 sessions). Assuming a null hypotheses of 26 sessions (the minimum number of sessions required to attend over 12 weeks), and a standard deviation of 8, we will have 89% power to detect a significant difference in a one-sample Zoom will be used for the EP to deliver the live personal training sessions to participants during Part 1. A secure HIPAA-compliant link will be emailed to the participant prior to the exercise sessions. When the participant clicks on the link, a video call with the EP will start on their device. The participant may need to download software at the beginning of their first exercise session if they have not previously used Zoom on that device. Participants will not need to create an account with Zoom. A Zoom connection test will be scheduled prior to the first exercise session. During this connection test, the study participant and EP will have a Zoom call to test audio and video setup and connection.During exercise sessions, the EP will record their screen and will save the recording to a Box cloud drive. These recordings will include audio and visual components for both the EP and the participant. Recordings will be password protected. At the start of Part 2, the EP will select 15 recordings that represent varied types of exercises to be shared with the participant. Study staff will provide the participant with a USB drive, or give them access to a Box drive online that contains these selected recordings of sessions performed during Part 1. If using the USB drive, it will be encrypted and the participants will be given the password. If using Box, participants will be emailed a secure HIPAA-compliant link to the cloud drive containing their videos, which will also be password protected.Patients will be asked to wear a study-provided Fitbit wrist-worn activity monitor for the duration of the intervention for the purpose of tracking their daily activity and sleep over the course of the study (Parts 1 and 2). Patients will be asked to install the Fitbit application on their mobile device and use a patient-specific, study-created HIPAA-compliant Fitbit account. Study staff will assist participants in setting up their Fitbit during their baseline visit. Once Fitbit data is synced to the Fitbit, data will be stored in and downloaded from the Fitbit and monitored via Fitabase (Fitbit data collection and management for research projects). Clinical research coordinators will download activity data following the mid-study and end of study visits. Throughout the study, the EP will remind the participant to wear the Fitbit and to sync their device after each exercise session. If the Fitbit is not synced after reminders, the clinical research coordinator will contact the participant and ask them to sync it. During Part 2 of the intervention, the clinical research coordinator will remind the participants to sync, wear, and charge their Fitbit.The participant and the EP will meet via Zoom three times every week during Part 1 (EP-directed exercise). The participant can choose to have their three weekly sessions on any weekday. Sessions are allowed to be completed on consecutive days. These sessions will include a 5 min warm-up, 40 min moderate intensity exercise consisting of aerobic “cardio spurts” , chair exercises, resistance training with study provided resistance bands, and a 5 min cool down. The 40 min of moderate intensity exercise will consist of 10 exercises continued for 1 min, each with 15 s of rest between exercises. The intervention will include exercises that target the muscle groups back/biceps, chest/shoulders/triceps, legs, abdominals, and overall aerobics. After the 10 exercises are completed, the participants will be given 2 min of rest, before repeating the 10 exercises 2 more times, totaling 3 rounds. The total weekly exercise sessions will result in 150 min of activity. The EP will rotate between different exercises for each participant to work different muscle groups at different sessions. The EP will personalize the exercises to the patient’s fitness and ability level to maintain a moderate intensity level. Participants will be provided with exercise Therabands of various strengths at the baseline visit, and will gradually move up in band resistance strength throughout the study. The yellow Theraband will be the lightest (1.3 kg of resistance at 100% elongation), then red (1.7 kg of resistance at 100% elongation), green (2.1 kg of resistance at 100% elongation), and blue (2.6 kg of resistance at 100% elongation). Participants will start at different Theraband strengths based on their baselines assessments, or even double up the Therabands if more of a challenge is necessary. The participants will be expected to use a four-legged sturdy chair at their home  to complete chair-based exercises. Participants will be expected to wear exercise-appropriate clothing. Participants will be expected to have a clear 5 × 6-foot space available to exercise in. Participants may use a mat during the exercise, but it is not required. The intervention is tailored to each participant’s physical fitness and ability levels. During Part 2 (participant-directed exercise), participants will have access to videos of past training sessions with the EP, but no training sessions with the EP will occur. Then, 10–15 of the best videos will be chosen by the EP that will best support the participant in moving forward with self-directed exercise. Adverse events will be documented and reported in a timely manner to ensure the safety of subjects enrolled in the study. Adverse events  will be documented for each exercise session and addressed immediately. Additional questions about adverse events will also be reviewed and determined if related to the study by an MD investigator following the mid-study and end of study visits. If a participant experiences an injury, issue, or adverse event and remains in the study during Part 1, the EP will tailor their exercise regimen so as not to exacerbate the injury or illness. If a participant becomes injured during Part 2, they will be instructed to self-tailor the exercises to fit their abilities, and to prioritize their healthcare providers’ advice regarding exercise.Patients may skip no more than 30% (no more than 10 out of 36 sessions) of the expected exercise sessions after starting Part 1. If a participant misses more than 30% of sessions, the participant will be removed from the study. Participants who miss more than 2 weeks of exercise sessions in a row after starting Part 1 (6 sessions in a row) will be removed from the study. Detraining can be seen as early as two weeks after not participating in exercise, which is the rationale behind participant removal after two consecutive weeks of missed sessions ,22. PatiPatients can be taken off the study treatment and/or study at any time at their own request, or they may be withdrawn at the discretion of an investigator for safety, behavioral, or administrative reasons. The reason(s) for discontinuation will be documented and may include voluntarily patient withdraws (follow-up permitted); patient withdraws consent (termination of treatment and follow-up); patient is unable to comply with protocol requirements; patient sustains a major injury or is diagnosed with a new major disease that makes continuation of the protocol unsafe; treating or study physician determines continued participation in the study would not be in the patient’s best interest; patient sustains an injury or develops an issue that makes them unable to be independently ambulatory ; (Part 1 only) the patient skips 30% (more than 10 out of 36 sessions) of the expected exercise sessions, or participant misses more than two weeks’ exercise sessions in a row (6 sessions in a row). Sessions skipped due to a national holiday or cancellation by study staff (a maximum of 3 sessions) will be made up at the end Part 1 and will not count toward this total. Patients will be removed from the study if any of the criteria listed apply. The primary investigator will be notified and will document the reason for study removal and the date the patient was removed on the Case Report Form. The patient will be asked to return for an end of study visit. Patients who are removed from the study or who withdraw during Part 1 may complete a final discussion with the EP regarding personalized exercise recommendations and general physical activity goals. Subjects who withdraw from the study treatment prior to starting study intervention will be replaced.± 14 days from first exercise session with EP. All concomitant medications and supplements will be collected through medical record review at the baseline, mid-study, and end of study visits.All surveys will be completed on paper or online via REDCap, according to participant preference. All physical assessments will be completed in-person during a study appointment at the host cancer center. Baseline in-person visits may be completed at any point between determination of eligibility and first exercise session with the EP. All baseline procedures must be completed within 14 days prior to the first exercise session with EP. Mid-study visits will be completed up to 10 days following the last exercise session with EP, and end of study visits will be completed at 24 weeks A demographics questionnaire will include six questions regarding race, education, employment, household income, and how many people live in the household. Demographics will be collected from charts and via demographics questionnaires. The Physical Activity Readiness Questionnaire (PAR-Q+) will be used for participants to self-report exercise readiness and safety during screening. The PAR-Q+ is a reliable and valid questionnaire that will be administered and completed on paper or online via REDCap ,24. PartThe primary outcome will assess feasibility through adherence. Attendance will be used as the primary outcome for statistical purposes and power calculations, but retention, enrollment, and manipulation checks will be considered as well. Adherence will be measured through attendance, which is defined as frequency of the 36 Zoom exercise sessions attended and fully completed over the 12-week study time period. A fully completed session is defined as participation of the full exercise session.Retention will be measured as a secondary outcome by the percentage of patients approached who sign consent. Retention is defined as participation from baseline through to the final assessment. Enrollment will be measured as a secondary outcome and is defined as the percentage of patients approached who sign consent. A final measurement consists of a manipulation check, as well as sound and visual clarity checks for Zoom sessions. After each session, the EP will ask the participant to measure sound and visual clarity using a scale of 0–100%. A score of 0% would be the worst sound and visual clarity, while 100% would be the best clarity.2 max during the baseline, mid-study, and end of study visit. The test involves slowly increasing treadmill  incline grade to 15% and speed to 7 MPH to assess cardiorespiratory responses, including HR, BP, and perceived exertion. A maximum of 15% incline grade will be used due to the treadmill’s capability of only being able to lift to a 15% incline. This modifies the Bruce submaximal protocol which usually calls for a maximum of 26% incline grade. The rating of perceived exertion on a 1–10 scale will be measured every minute. HR will be monitored every minute using a pulse oximeter. BP will be measured every 3 min. Each stage of the Bruce submaximal lasts 3 min each, unless the participants’ heart rate is not within 6 beats of the previous minute. If the HR is not within range, the stage will be continued for another minute. During each stage, the speed and incline of the treadmill will increase until the participant cannot continue, or until the participants’ reaches 85% of their maximum HR.Fitness outcomes are to be presented in the order in which they are collected. Heart rate will be measured during the baseline, mid-study, and end of study visit. Heart rate (HR) will be obtained after the participant has been sitting for at least 5 min . Heart rThe Functional Assessment of Cancer Therapy—General (FACT-G) version 4 will be administered during the baseline, mid-study, and end of study visits. The 27 questions in FACT-G measure physical, social, emotional, and functional well-being in cancer patients. Items are assessed on a 5-point scale Likert-type scale. It has been demonstrated that participants have a better quality of life if they score higher on the FACT-G. The FACT-G has been shown to have high coefficients of reliability and validity for patients with mixed cancer diagnoses who are 18 years or older ,31,32. TFor all quantitative data collected, means and standard deviations will be computed for continuous measures and proportions for count data. The average number of Zoom sessions attended will be tested in a one-sample T-test against the null hypothesis of 26, which is the minimum number of sessions that need to be attended over 12 weeks to maintain 70% of sessions completed. All quantitative data  will be tested for changes over time with mixed-model regression with random subject-intercept, and patient sex as a covariate. All point estimates of change over time will be computed with 95% confidence intervals. All consented patients will be considered in the analysis.Qualitative interview data will be analyzed using thematic analysis with a focus on understanding participant experiences and how to improve the intervention for future delivery . The rigFor the primary outcomes, we anticipate a strong adherence of 70% (26 out of 36 sessions) for Part 1, the supervised exercise sessions. During Part 2, the participant-directed exercise, we anticipate that adherence to exercise sessions may decrease.For the secondary outcomes, we anticipate improvements at 3 months, and maintenance of improvements at 6 months.Given the nature of qualitative research, no a priori hypotheses were developed for the qualitative aspects of the study.


This article was migrated. The article was marked as recommended.Active learning has been proven as an effective teaching method that increases students’ academic performance, satisfaction, and promotes life-long learning. A previous study showed that a peer lecture was time-consuming for both faculty members and students without any increase in knowledge outcome achievement of students when comparing to faculty lectures. Therefore, we replaced all peer teachings, taught in 2015, with faculty lectures and small group discussions in an organ-system part which focused mainly on applications of medical microbiology in 2016. The organ-system part was further divided into 3 sections according to type of organ systems. The knowledge outcome achievement was compared using MCQ scores. Peer lectures were mainly used as a teaching method only in 2015  while none of them was used in 2016. On the other hand, SDG were mainly used as a teaching method only in 2016 . Students of 2016 had significantly higher average MCQ scores than those of 2015 only in the section 3 . There was no significant difference in section 1, section 2, and overall MCQ scores. Apart from routine teaching preparation, there was no out-of-class preparation time for faculty lectures and SGD while peer teaching required at least 0.5-2 hours of extra out-of-class preparation time from at least 1 faculty member and 10-12 students per 1 hour of teaching. In conclusion, SGD provided equal or more knowledge outcome achievement of the student with less time-consuming than peer lecture. Therefore, at least in our teaching environment, SGD was proved to be a better option than a peer lecture for teaching applications of medical microbiology. Medical microbiology is a study of pathogenic microorganisms  and antimicrobial agents used against them  which were trained for both microbiological knowledge and presentation skills prior to each lecture session. The best peer lecturer was decided by online votes from all students in the class. This teaching method improves students’ understanding of the subject and overall satisfaction without compromising the knowledge gain.  is a student-centered, problem-based learning method where students in the same group brainstorm and discuss the best answers to the given problems while their teachers act mainly as facilitators. SGD has been proven to improve students’ understanding on the subject as well as their satisfaction  was implemented in the organ system part, which focused on application of basic medical knowledge for approaches and managements of patient-based problems, of medical microbiology course in 2016. Moreover, we canceled all peer lectures, taught in 2015, as they were time-consuming for both faculty members and students without any increase in knowledge outcome achievement of students  comparing to those of the prior sections, section 1 (6:12) and section 2 (10:18),In summary, apart from the canceling 43 hours of peer lectures, facultylectures were increased from 4 to 16 hours and 51 hours of SGD class were implemented in the class of 2016. This study was ethically approved by Siriraj Institutional Review Board under certificate number 589/2560 (Exempt).https://www.socscistatistics.com/tests/chisquare/Default2.aspx) accessed in September 2018. Statistical significance was achieved at p-value < 0.05.Achievement of knowledge outcomes was evaluated by multiple-choice questions (MCQ) at the end of each section. MCQ of both years were optimized so that the difficulties and tested outcome were similar. A stem was typically an example of a patient problem. For example, “A 30-year-old man presented with watery diarrhea for 1 day. He had a history of traveling to India 5 days ago.” A question was one of the topic taught in each class  includinTo minimize bias due to possible difference in the initial capability of students between each year, the MCQ score in each section in the year 2015 was normalized by multiplying with the ratio of “general concept” MCQ score of year 2016/2015.rd year medical student were largely 20-21 year old.Total of 615 medical students were included: 315 and 310 students studied medical microbiology in 2015 and 2016, respectively. No medical students were excluded from this study. According to Thai education regulation, student typically enters medical school right after finishing high-school. Therefore, student age of the 3Students of 2016 had significantly higher average MCQ score than those of 2015 only in the section 3 . However, there was no significant difference in section 1, section 2, and overall scores Peer lectures were mainly used as a teaching method in the year 2015  and none of them was used in the year 2016. In fact, all classes in 2015 were taught by one-way lectures as the peer lectures was also considered one-way to the other students in the classes. On the other hand, SDG were mainly used as a teaching method in the year 2016  and none of them was used in the year 2015. There were more SGD in the section 3 than those in the section 1  and section 2 . Apart from a routine teaching preparation, there was no out-of-class preparation time for faculty lecture and SGD while peer teaching required at least 0.5-2 hours of extra out-of-class preparation time from at least 1 faculties and 10-12 students (1-2 groups) per 1 hour of teaching . DetailsTypically, active learning improves knowledge outcome , such abSuch increase in the knowledge outcome achievement did come with a cost, more teaching hours were required for the SGD year of 2016 when comparing to the peer lecture year of 2015 (67 VS 47 hours). However, as the extra out-of-class time was not needed for the SGD. In fact, approximately, twice the teaching time (94 hours) were actually required for the peer lecture to have at least equal knowledge outcome achievement. Therefore, this fact resulted in SGD became a less-time consuming method than the peer teaching.Limitations of this study were as followed: firstly, as students also learned other subjects along with the microbiology course throughout the year, changes in other subject might influence microbiology scores. For example, a symptomatology subject, which taught general approach to patient, would directly influence how students approach patients with localizing infections; secondly, though normalized by the general concept scores, differences in non-microbiological knowledge and skills of the two group of students might also influence the results of this studies. For example, a recent study revealed that students with stronger critical thinking dispositions performed better in the problem-based learning process and achieved higher scores  than allowing students to work through a presentation on their own. Good lecturing requires skills that a student is less likely to have. I also remain concerned about the evaluation by MCQ testing, as this may reflect only simple knowledge recall. I find it unsurprising that small group teaching produced better results, even by the methods used, but really do wonder why this model of peer teaching would even be considered. It is a long way from peer teaching methods used in most contemporary hybrid curriculum programs, where students leading discussions with access to other learning resources is more commonly seen.Reviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 4 stars out of 5 Thank you for working on a new version of paper, which, based on the suggestions of the reviewers, has actually improved the quality of the studyReviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 1 stars out of 5 this is a practical study in medical education. the findings are encouraging in support of student centered learning. I would have liked to learn more on qualitative issues of learners feelings about SGD.Reviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 4 stars out of 5 A very detailed and informative study. I would recommend people working in same discipline to read and conduct further research on this important aspect of learning medicine.Reviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 3 stars out of 5 I found that this is an interesting paper dealing with the intriguing issue of finding innovative strategies to teach basic sciences in the preclinical medical years. My general comment is that I am not surprised to see that the authors replaced peer lessons with small group discussions. In my view, peer lessons can be offered as pills, possibly provided by medical students of the 5th or 6th year or residents, but are not adequate to cover an entire course. In terms of examination scores, I think that comparing two cohorts belonging to different years has substantial limitation and is not very informative. My specific comment is that the paper would benefit from more detailed description of the methodological procedures used in the study. For example, the authors state that there was no out-of-class preparation time for faculty lectures and SDG. But SDGs are extremely complex to organize. How was the discussion planned? Did discussions involve clinicians and basic science teachers simultaneously? How did SDGs match with lessons? Why did the authors plan such a big discrepancy between section 3 (2 faculty lectures and 21 SGDs) and section 2 (10 lectures and 18 SGDs)? Was it done with any specific purpose? I think adding such information could provide an additional value to the manuscript.Reviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 2 stars out of 5 This is an interesting paper that may raise more questions than it attempts to answer. I work in a different context, where the idea of 'peer lectures', as I understand the term, would most likely not be acceptable. Could the authors please provide more information on what a peer lecture is? I am not a fan of lectures, unless they address topical, current or possibly contentious issues that can be clarified by someone who is relatively expert in the topic. Students are better at asking challenging and clarifying questions, and discussing issues to develop a shared understanding. While individual students may benefit from preparing a lecture, the recipients may be better off reading the literature themselves. Hence personally I wold always prefer to schedule small group discussions with students and, ideally, a faculty member. However, when it comes to the finding that SGD is better than peer lectures because of changes in examination scores from one year to the next, I am not sure that the authors have robust proof. Different cohorts, different learning experiences, different examinations etc mean that direct comparison is not really appropriate, particularly when the peer lectures were onlt 40-odd hours of the whole year. Care must be taken not to over-interpret data.Reviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 4 stars out of 5 Thank you for the invitation to review this interesting article. It is sad to hear that the authors gave up the great idea of having peer lectures. That has been a great idea and, no doubt, preapring such leactures has been a great learning experience fot the students. However, as the authors noticed, the lecture is still rather passive learning for the other students. Thus, replacing the lectures with small group discussion is a pedagogically sound solution. Discussing clinically relevant problems will enhance the students problem solving skills, depending on the structure of the sessions. It will be interesting to know the next steps of this development.Reviewer Expertise:NANo decision status is available


Determining the presence of water molecules at protein–ligandinterfaces is still a challenging task in free-energy calculations.The inappropriate placement of water molecules results in the stabilizationof wrong conformational orientations of the ligand. With classicalalchemical perturbation methods, such as thermodynamic integration(TI), it is essential to know the amount of water molecules in theactive site of the respective ligands. However, the resolution ofthe crystal structure and the correct assignment of the electron densitydo not always lead to a clear placement of water molecules. In thiswork, we apply the one-step perturbation method named acceleratedenveloping distribution sampling (AEDS) to determine the presenceof water molecules in the active site by probing them in a fast andstraightforward way. Based on these results, we combined the AEDSmethod with standard TI to calculate accurate binding free energiesin the presence of buried water molecules. The main idea is to perturbthe water molecules with AEDS such that they are allowed to alternatebetween regular water molecules and non-interacting dummy particleswhile treating the ligand with TI over an alchemical pathway. We demonstratethe use of AEDS to probe the presence of water molecules for six differenttest systems. For one of these, previous calculations showed difficultiesto reproduce the experimental binding free energies, and here, weuse the combined TI–AEDS approach to tackle these issues. In the case of infinite sampling,molecular dynamics (MD) should account for proper sampling throughoutthe simulation, including water molecule displacements and placements.16 However, in practice, MD simulations are restrictedin sampling, usually ranging from ns to μs timescales. Thistime limitation becomes more pronounced when one wants to use MD inthe lead optimization phase of the drug discovery process since multipledifferent compounds need to be evaluated.In recent years, alchemical free-energymethods have increasinglyfound realistic application in drug design efforts.17 In RBFE calculations, thebinding affinity between two similar ligands A and B is usually obtainedby transforming ligand A into ligand B in both the bound state (ligand–proteincomplex in solution) and the free state (ligands in solution) overa set of non-physical alchemical intermediate states.18 However, when perturbing one ligand into another, the chemicalproperties in some parts of the ligands may change, which could leadto displacement, removal, or more room for water molecules. Thesefactors are of great importance since, as already described, the restrictedmovement of water molecules can influence the overall accuracy inthe relative binding free energies by strengthening different ligand–proteininteractions.5 Additionally, due to theshort simulation length used in alchemical methods, changes in watermolecule preferences between both ligands to compare may lead to watermolecules that are either trapped or unable to enter the active site.In the past, there have been various methods developed to accuratelyaddress these challenges.27 A few of these methods did already deliver good results and havebeen found to be used in RBFE workflows.20To avoid having tosimulate binding and unbinding events, whichwould require long simulation time scales, relative binding free energy(RBFE) techniques employing alchemical perturbations are commonlyused instead.28 when coupled to MD, as implementedin BLUES,25 can enhance the sampling inburied binding pockets,where water molecules can hop between energy basins. BLUES uses adefined spherical region around the ligand, where water hopping isallowed and additional relaxation steps are included to improve theacceptance of NCMC moves. Furthermore, there are grand canonical MonteCarlo (GCMC) methods which allow for the fluctuation by allowing insertionor deletion of water molecules in any given region. GCMC allows forthe number of particles to fluctuate at a specific chemical potentialand by using grand-canonical integration, one can incorporate thethermodynamics of the water molecules in calculating free energiesof water networks.30 Recently,a combination of grand canonical nonequilibrium candidate Monte Carlo22 was developed, which exploits the smaller bufferregion from GCMC and the relaxation step during insertions/deletionsfrom NCMC. In general, these methods enhance the water molecule samplingprocess compared to normal MD, and GCMC seems to be more efficientin doing so.31Among these methods, the nonequilibrium candidate Monte Carlo(NCMC),32 or Bennett acceptance ratio33 while applyingcertain correction terms. For instance, if we consider that ligandA favors one water molecule in the active site and ligand B none,we can correct the RBFE difference by expanding the classical thermodynamiccycle to further include a correction term for removing one watermolecule when perturbing ligand A into ligand B. This approach deliversonly reasonable results when the correct number and the approximateposition of the water molecules are known from experimental data  or other computational methods. It is important to notethat water molecules reported in crystal structures are to be treatedcarefully since especially partially occupied hydration sites cloudthe resulting electron density, which makes it hence more difficultto assign.34Additionally, whenassuming that the correct amount of water moleculesis known, a straightforward approach is to apply standard RBFE calculationssuch as thermodynamic integration (TI)36 Using this method allowsfor sampling of the relevant end states. In this case, we define differentamounts of water molecules being active. By using the accelerationparameters obtained from a solvent run, one can probe the presenceof the molecule of interest in a quick and system-independent manner.37 Finally, we showcase the potential of usingAEDS to treat water molecules in free-energy perturbation calculationsby combining the AEDS method with standard TI and comparing them withthe free energies obtained from other simulation setups.38In this work, we suggestto apply accelerated enveloping distributionsampling (AEDS) to probe solvation shells in the binding pocket bydetermining how favorable those water molecules are in the respectivesystem. AEDS is a one-step perturbation method that uses a referenceHamiltonian, which is a combination of different Boltzmann-weightedend-state Hamiltonians.7 A total of six pairs of ligands were selected.Each pair of compounds features a minor chemical modification thatleads to the displacement of one or two buried water molecules inthe active site without producing any major conformational change.In this work, we designate these ligands using the same naming conventionsas in the previous work.7 All the inputprotein systems were prepared using the available crystal structuresin the protein databank. The used crystal structures are the following:Kung-13 pdb 3RLQ; Kung-16 pdb 3RLR;39 Davies-13b pdb 4FCP;40 Woodhead-1 pdb 2XAB; Woodhead-2 pdb: 2XJG;41 Cumming-4jpdb: 4DJW; Cumming-4bpdb: 4DJV;42 Chen-5d pdb: 3STD;43 Smith-8pdb: 4ZLZ; andSmith-11 pdb: 4Z3V.44 All the water molecules present inthe crystal structures were kept. For the systems for which no crystalstructures were available, the starting coordinates were obtainedby modifying the closest available crystals. In the case of Chen-3d,the structure of Chen-5d (3STD) was used to determine the proteinand ligand orientation. The water molecule of the hydration shellwas determined using the crystal structure of a closely related inhibitor,pdb: 5STD.43 This compound has a similar binding pose asthe one under study and the same functional groups pointing to thehydration site, providing a reasonable guess for the starting coordinatesof the water molecules. For system Davies-15b, the nitrile group wasplaced based on geometry and then allowed to relax during minimization.For all the pairs of the system that displayed no water moleculesin their hydration sites, an additional dummy water molecule was placedby aligning the structure to the other member of the pair in whichthe water molecule was present. This dummy water molecule was usedas the starting coordinates for the AEDS water molecule during thewater probing runs. Both the protein and ligands were parametrizedusing the GROMOS 54a7 forcefield.45 Force-fieldparameters for the ligands were assigned based on analogy to similarfunctional groups in the force field and are available in the Supporting Information.The benchmarks used for this workhave been taken from the work of Wahl and Smieško.46 molecules,leaving a minimum distance of 0.8 nm between the solute and the boxwalls. The water box positions were relaxed in a steepest descentminimization with position restraints on all the solute atoms andcrystallographic water molecules. Counter ions were added by replacingwater molecules with the most favorable electrostatic potential toreach charge neutralization using the program ion provided in theGROMOS++ package.47Short energy minimizationswere performed for all the systems using the steepest-descent algorithmin a vacuum. The systems were placed in a periodic cubic water boxof simple point charge (SPC) water4 to 0.0 kJ mol–1 nm–2.After temperature equilibration, an additional equilibration of 1ns without position restraints and a constant pressure of 1 atm withan isothermal compressibility of 4.575 × 10–4 kJ–1 mol nm3 was performed. After equilibration,the atom positional root-mean-square deviation of the backbone atomsand ligand atoms was monitored to ensure that the systems were properlyequilibrated.Then, the systemswere equilibrated with initial random velocitiessampled from a Maxwell–Boltzmann distribution at 60 K and heatedup to 300 K in five discrete steps. While heating up the system, positionrestraints on the solute atoms were reduced from 2.5 × 1048 was used to maintainthe bond lengths at their optimal values. Long-range electrostaticinteractions beyond a cut-off of 1.4 nm were truncated and approximatedby a generalized reaction field49 witha relative dielectric permittivity of 61.50 Nonbonded interactions up to a distance of 0.8 nm were computedat every time step using a pairlist that was updated every 10 fs.Interactions up to 1.4 nm were computed at pairlist updates and keptconstant in between.All the production simulations were performedat a constant temperatureof 300 K and pressure of 1 atm. Newton’s equations of motionwere integrated using the leapfrog algorithm with a time step of 2fs. The SHAKE algorithm36 of the enveloping distribution sampling algorithm52 was used to probe water molecules by assessing the fractional occupancyof the water molecules of interest. The Hamiltonian of these watermolecules was replaced by a reference state HR, which is obtained by combining the Hamiltonian of the differentstates of interest, Hi. For this case, the Hamiltonian of an SPC water molecule (coupledstate) and the Hamiltonian of a dummy water (dummy or decoupled state)are combined to form the reference Hamiltonian. For the cases in whichtwo water molecules were probed at the same time, a reference stateconsisting of all the possible combinations of coupled and dummy watermolecules was built instead, resulting in four end states . To ensure equal samplingbetween all states, an energy offset, ΔFiR, is used to pull both energy minima at the same level (35RT being the gas constantmultiplied by the temperature (300 K). Building such a reference stateresults in an energy landscape with large energy barriers betweenthe states that can hamper correct sampling of all the states of interest.To solve this issue, AEDS uses a harmonic boosting potential definedas follows can be obtained using a search algorithm.37 During the search algorithm, the end-states being sampled are monitoredby comparing the values of Emax. Emin is calculated based on the fluctuationsof the values of Hi in 36 Theend-state sampling times can be obtained by computing the weightsof the different states over all of the simulation frames.37The accelerated versionme level 1:351with follows 2:362wher–1 nm–2. The final acceleration parameters as wellas the distance restraints used can be found in the Supporting Information.To determine the acceleration parametersfor the AEDS water molecules, a complete search of 100 ns was performedin bulk water. During the search runs, the offsets are estimated fromthe free-energy differences between the accelerated end-states andthe reference state. The number of frames that will be used to determinethe offsets are determined by a linearly increasing the memory decayfunction, going from 500 steps (1 ps) to 500,000 steps (1 ns). Anadditional production run of 100 ns was performed to corroborate thatthe selected acceleration parameters allowed quick fluctuations betweenstates and roughly equal sampling times. The parameters obtained fromthis search in solvent were then used for all the hydration sites.To avoid the AEDS water molecule from leaving the active site dueto its partially decoupled nature, distance restraints to the proteinbackbone were used, with a force constant of 1500 kJ mol–1 nm–2 at the λwindow with the coupled water molecule to 1500 kJ mol–1 nm–2 at the λ window in which the watermolecule was decoupled. Each perturbed water molecule was restrainedby a harmonic distance restraint with a reference distance of 0 relativeto a single virtual site, which was defined using the center of geometryof the selected protein atoms to avoid indirectly bonding those atoms.The free energy of removing an SPC water molecule from bulk water(26.8 kJ/mol) was added to the resulting ΔΔG from the alchemical perturbations for each water molecule that wasperturbed alongside the ligand. Additionally, the effect of the distancerestraint was corrected analytically using 54K is the force constantused for the distance restraining and –2 nm3).Relative binding free-energy calculationswere performed for ligand pair Woodhead 1 and 2, bound to Heat shockprotein 90 (Hsp90). The RBFE was calculated from thermodynamic cyclesin which alchemical perturbations were performed twice: once for theligand bound to the protein and once for the ligand in solvent. Toinfer the effect to the free energy of the buried water molecules,different combinations of water molecules being present or perturbedwere used alongside the ligand perturbation, resulting in the followingsetups: both crystallographic water molecules  being presentunperturbed, both crystallographic water molecules being absent, bothcrystallographic water molecules being perturbed (going from the coupledto the decoupled state), and both water molecules being present andwater 2144 being perturbed. For all the perturbations in which a watermolecule was perturbed in addition to the ligand, perturbed distancerestraints coupled to λ were used, ranging from a force constantof 0 kJ mol55 and a singletopology approach.56 In extended TI, thederivatives of the Hamiltonian with respect to λ are predictedfor a number of additional λ-values than actually simulated,leading to smoother curves and more precise free-energy estimates.55 The number of precalculated λ-points wasset to 101. A soft-core potential was used for the Lennard-Jones (LJ)and Coulomb + reaction field (CRF) interactions, with soft-core parametersαLJ = 0.5 and αCRF = 0.5 nm2 for the atoms being perturbed. A total of 11 equally spacedλ-values of 20 ps of equilibration and 1 ns of production wereused. Both the electrostatic and Lennard-Jones interactions were perturbedat the same time. Backward simulations for all the perturbations wereperformed starting from the last frame of λ-point 1.0.All the perturbations were performed usingthe extended TI methodologyHS correspondsto the Hamiltonian of the system excluding the AEDS reference stateand HR* correspond to the accelerated AEDS Hamiltonian. The partialderivative of the accelerated AEDS Hamiltonian with respect to λcan be obtained from HR, are obtained from eq The AEDS algorithm was extended to make it compatiblewith the existing TI code. In the TI-AEDS simulations, the water moleculeof interest was treated with the AEDS algorithm while the ligand perturbationwas treated with TI. The free energy difference can be calculatedaccording to G of bindingbut also the ΔG of each individual path.The performed TI-AEDS simulations followa similar simulation setup to the one described for the extended TIsimulations. The number of λ windows was increased to 41 equallyspaced λ-points of 5 ns each in order to achieve converged freeenergies. The increase in λ-points was needed since the currentTI-AEDS implementation is not compatible with the existing extendedTI one which provides convergence with less simulated λ-points.The perturbation topology was changed to a double topology approachto avoid perturbing bonds, and the distance restraint was appliedconstantly on the AEDS water molecule instead of using a distancerestraint coupled to λ. Additional TI simulations followingthe same setup as described but treating the water molecule with TIwere performed. These simulations were used to be able to furtherassess the accuracy and consistency of the TI-AEDS simulations bycomparing not only the final ΔΔ7 This benchmark consists of six pairs of compoundson four different proteins, in which each pair of compounds containsa small modification which results in a different number of buriedwater molecules present in the active site.44 Incorrect sampling of the solvation shell of these ligands leadsto wrong free energy of binding estimations and to significant hysteresisbetween replicates depending on the initial solvation.8To test the capabilities of AEDSto probe the water molecules of interest by assessing their fractionaloccupancy, a previously reported set of benchmark systems designedto study changes in solvation of active sites was used.7 but with the watermolecules known to be different between both members of a pair ofcompounds replaced by AEDS waters. An AEDS water is an artificialmolecule consisting of the combination of the Hamiltonians of a watermolecule and a dummy water molecule. Thanks to this, the AEDS waterhas the capabilities of switching between the coupled and decoupledstates (dummy).The initial structures were prepared as described by Wahland Smieško36 achieving, in this way, fast switching between the end-states ofinterest. In the standard AEDS approach, since these offsets and accelerationrange are system-dependent, they can be obtained during a preliminarysearch run.36 First, a complete searchof all the AEDS parameters is performed in bulk water. Then, a searchof only the energy offsets is performed in the protein, but keepingthe acceleration range to the values obtained from the search in bulkwater to allow for a faster convergence.37 In this work, the AEDS methodology is used as a screening tool instead.For this approach, the acceleration parameters are searched only oncein bulk water and then used in all the systems. Using the parametersoptimized in bulk water on the protein results in unequal samplingof the different end-states, in which the more energetically favorableend-states are sampled more often. This shift in observed sampling,when compared to the sampling in bulk water, can be used to inferthe likelihood of the presence of the molecules of interest.37 In this work, we use this screening capabilityof AEDS to probe or asses the fractional occupancy of buried watermolecules by substituting them with AEDS waters. In summary, the AEDSwater is able to freely shift between coupled and decoupled statesduring the simulation based on its surroundings, behaving similarto a chemostat.37However, the resulting energy barriers fromcombining both Hamiltonianscannot be overcome during a conventional simulation, which would leadto simulations stuck in one state, and even if this barrier is crossed,the different depth of the energy minima of both states would leadto simulations that spend most of the time in one end-state only.To overcome this limitation, AEDS uses an energy offset to pull theminima to a similar height, ensuring a more equal sampling of thestates of interest, and then defines an acceleration region in whicha boosting potential is added to decrease the energy barriers,Supporting Information. The obtained acceleration parameters allowed for equal samplingof all the end-states of interest with rapid fluctuations betweenthem with and an average lifetime of 6.7 ± 1.3 ps for both statesin the simulation with one AEDS water and 5.5 ± 3.5 ps for thesimulation with two AEDS water molecules. The obtained free energyof solvation using these parameters was 26.6 ± 0.5 kJ/mol, ingood agreement with the free energy of solvation obtained with extendedTI (26.8 ± 0.3 kJ/mol). Each ligand–protein complex understudy was then simulated individually with AEDS water molecules for10 ns. The sampling times of the coupled and decoupled states foreach AEDS water molecule was monitored by using their respective contributionto the energies of every configuration in the simulation.37For the case ofthe AEDS waters, since the molecule to be probedis always the same, a water molecule, the acceleration parametersobtained in bulk can be used in a system-independent manner to probehydration sites of any protein. AEDS search runs of 100 ns were performedto guarantee that the obtained acceleration parameters are well converged.The obtained acceleration parameters were then validated with 100ns AEDS production runs, see the 44 as depicted in Supporting Information.The resulting sampling times closely matchedthe observed numberof water molecules in the crystal structures of the different pairsof ligands under study,43 in which there is one water molecule being displacedfromthe active site, shows a clear preference to the number of water moleculesseen in the crystal structure, with the water molecule being present100% of the simulation time for compound Chen-3d and only 4.8% ofthe simulation time for compound Chen-5d.The pair of compounds (Chen-3d–Chen-5d),42 showeda more diverse presence of water molecules.Cumming-4j showed 59.6% of sampling time with the water molecule beingpresent, and this water molecule showed clear preference for Cumming-4jwhen compared to Cumming-4b, in which the water molecule was observedonly 19.9% of the simulation time. A similar preference can be observedfrom the free energies, with a binding free energy of the water moleculeof 0 kJ/mol for Cumming-4j and an unfavorable binding free energyof 4 kJ/mol for the water molecule in the presence of Cumming-4b.This pair of compounds is known to be challenging to evaluate sincethe pyridine ring of ligand Cumming-4j, with which the water moleculebeing probed interacts, can flip during the simulation.7 To better account for the conformational changeof Cumming-4j, the presence of the AEDS water was traced as a functionof the ring orientation observed, The pair of compounds(Cumming-4j–Cumming-4b)40 and (Smith-8–Smith-11)44 alsoshowed a partial hydration for the ligands in which a water moleculeis expected to be present, Davies-13b and Smith-8, with the coupledwater state being present 40.6 and 56.3% of the simulation times,respectively. The obtained binding free energies for the water moleculein Davies-13b and Smith-8 systems were slightly unfavorable, with6 and 3 kJ/mol, respectively. Although longer simulations would beneeded to obtain converged free energies, the obtained values alsohint that the water molecule probed has a similar preference to bein the solvent as in the active site. When compared to their counterparts,there is a clear difference in the water molecule preference, withcompounds Davies-15b and Smith-11 only having the water molecule presentfor less than 1% of the total simulation time. In the case of Davies-13b,it is difficult to assess the real occupancy of that hydration sitesince there is no crystal structure resolved for this ligand. However,for Smith-8, there is a crystal structure available with a clear occupancyfor that water molecule.44 Occurrence ofthe water molecules around 50% indicate that the water molecules behavecomparably to bulk water and would have a binding affinity of roughly0 kJ/mol. A consequence could be that the impact of the water moleculefor the binding of those ligands is moderate.7The pairs (Davies-13b–Davies-15b)39 Forcompound Kung-13, both water molecules were present during most ofthe simulation time (83.5%), with water molecule 289 present for 93%and water molecule 249 for 90.2% of the simulation time, while compoundKung-16 showed mostly no water molecules present with 91.2% of thetime having no water molecules in those hydration sites.The pair of compounds (Kung-13–Kung-16), in which twowatermolecules are displaced, showed a clear preference for the coupledstate of the AEDS water that matched the one observed on the crystalstructures and electron density.Finally,the pair Woodhead-1–Woodhead-2 was the only pairof compounds for which the AEDS water preferences did not match theexperimentally observed ones. For Woodhead-1, both water moleculeswere present for most of the simulation time as seen in the crystal.However, for Woodhead-2, one of the water molecules, water molecule2295, was present for closely 100% of the simulation time; when inthe reported crystal structure, Woodhead-2 has no water moleculesin that hydration site.55 For all thesets of simulations, both forward and backward simulations were performedto identify possible sources of hysteresis due to the simulation setup.Each set of TI calculations used a different combination of the relevantwater molecules in the active site being present or perturbed in additionto the perturbation of the ligand. One calculation was performed withthe two water molecules being present continuously, without perturbingthem, one calculation while perturbing both relevant water moleculesat the same time ,one calculation with no water molecules present on either end-state,and one calculation perturbing only the water molecule 2144 (the watermolecule that AEDS predicted to be present).To study the discrepancy in the watermolecule preferences of the Woodhead-1–Woodhead-2 pair of compoundsin more detail, a set of four TI simulations using the extended TIalgorithm were performed.G of 6.9± 1.1 kJ/mol, only 1.5 kJ/mol away from the experimental oneof 8.4 kJ/mol.41 All the other sets ofsimulations produced free energies of more than 5 kJ/mol away fromthe experimental ones, see The TI setup thatproduced the closest values to the experimental relative free energyof binding was when water molecule 2144 was perturbed alongside theligand, with a calculated ΔΔ41 One explanation for why this water moleculeis not observed in the crystal structure of Woodhead-2 could be thatthis water molecule in particular is quite flexible, which would leadto a lack of electron density in the crystal structure. Alternatively,the current computational results could be an artifact of the forcefieldused.58 Regardless of the underlying reason, thenumber of water molecules that leads to TI results closer to the experimentalRBFE matches the water preferences obtained from the AEDS run, showingthat the AEDS water sampling is consistent with the forcefield used.This suggests that the ligand Woodhead-2 wouldhave an extra watermolecule in its solvation shell that forms hydrogen bonds with bothresidue ASP 93 and the ligand. This is in contrast to the crystalstructure, where there are no water molecules present at that site.In addition to probing the presence of water moleculesin different environments, AEDS waters can also be used in combinationwith other free-energy techniques to allow for a dynamic samplingof the water molecules of interest over time. To do so, the existingAEDS algorithm was expanded to interact with the TI algorithm of GROMOS.The ligand Woodhead-1 was perturbed to Woodhead-2 using the TI algorithm,while water molecule 2114, the one found to behave differently betweenboth ligands, was replaced with an AEDS water and allowed to fluctuate.By combining both AEDS and TI, one can obtain the thermodynamiccycle shown in To better compare the free energy obtained withthe TI–AEDSsetup, an additional TI simulation perturbing both water molecule2144 and the ligand was performed, this time without the extendedTI algorithm and using the same setup as the TI–AEDS run. Thefree energies obtained from the upper left-hand corner to the lowerright-hand corner of the cycle in H/∂λ⟩ profile with the presence of thewater molecule (H/∂λ⟩reaches the first peak (at λ = 0.05), the water presence startsto drop from nearly 100% of the time and decreases over the λwindows until finally reaching ∼0% at the second peak (at λ= 0.75) of ⟨∂H/∂λ⟩.This strongly suggests that the AEDS water can successfully respondto small changes in the environment.Additionally,the presence of water can also be traced along thedifferent λ simulations, allowing for a comparison of the ⟨∂molecule 5. At theX is the property toreweight (∂H/∂λ), Vy is the energy time series of either the water moleculebeing coupled or in the dummy state, and VR is the energy time series of the AEDS reference state.From the performed TI–AEDS simulation, itis also possibleto predict the free-energy difference of paths that are not directlysimulated, such as perturbing Woodhead-1 to Woodhead-2 with the watermolecule 2144 always present or absent, by reweighting the simulationsusing the corresponding time series, eq Tobetter assess the reliability of the reweighted simulations,two additional TI runs, one in which the water molecule 2144 was alwayspresent and one in which it was in the dummy state, were performedusing the same setup as in the TI–AEDS simulations. Both TIsimulations were in good agreement with the results obtained fromthe reweighted TI–AEDS simulations, with differences belowthe 2.0 kJ/mol range .S is the slope usedin the linear interpolation of the offset  and b is the constant term of the linear equation .In addition to the changing offset, the acceleration range was increased,and additional distance restraints to water molecule 2295 were usedto avoid the binding site from distorting due to the forced presenceor absence of the water molecule and the increased acceleration. Inthe evaluation of ∂H/∂λ, theadditional λ-derivative term, due to Finally,one can compute the cycle closures for the whole obtainedcycle. The cycle closures obtained are poor, having as high as 57.7kJ/mol of error due to incorrect estimation of the free energy ofsolvation of water molecule 2144 in the active site at λ 0.0and 1.0. This high error stems from the sampling observed of the AEDSwater in those λ windows. Since the other water state is neverseen, the estimation of the free energy becomes unreliable, as indicatedwith an asterisk in eq However, since usually one is not interested in describingthewhole cycle but rather in obtaining the free energy of the processof interest, this parameter optimization is not necessary and onecan use the parameters obtained in water in a transferable mannerto any protein. Overall, the tested setups provided free energieswith good agreement between each other.8 One simple solutionwould be to perturb the required number of water molecules alongsidethe ligand. However, in order to be able to do that, one must knowthe required amount of water molecules for each of the ligands beforestarting the simulations. Having this kind of knowledge about thesystem a priori might not be possible since there is no simple way,other than chemical intuition, to decide the amount of water moleculesrequired without a crystal structure, and even with a crystal structure,the presence of mobile water molecules may not be detected.34The change in water networks on the activesite can have a significantimpact on the calculation of relative protein–ligand bindingfree energies. This issue becomes more prevalent in alchemical perturbationsimulations, especially when the two ligands to compare require differentamounts of water molecules being buried in the active site. In alchemicalapproaches, the process of binding and unbinding is not simulated,resulting in buried water molecules being unable to leave or enterthe active site to correctly fulfil the precise water networks requiredfor each ligand under study, even when using relatively long simulations(50 ns per λ window).In this work, we have described a newuse of the AEDS method toquickly and accurately probe possible hydration sites in a system-independentmanner. By using an AEDS water molecule resulting from the combinationof a water molecule and a dummy particle, one can infer the likelihoodof the water molecule being present at every given location by lookingat the amount of time spent in each of the two states, acting in asimilar manner to a chemostat. This information can then be used todesign and set up further simulations for the system without relyingon any additional information. Additionally, the AEDS water can evenbe used alongside other alchemical methods to have a dynamic watermolecule that adapts to the ligand being perturbed, producing thesame free energies as one would obtain by perturbing both the ligandand the necessary water molecules. The described AEDS water screeningapproach can be used in a system-independent manner without requiringto perform additional search runs since the used parameters are onlydependent on the water molecule type. In this work, we have used AEDSto study the fractional occupancy of up to two SPC water molecules,but the described methodology could be easily expanded to other watermodels and to study additional water molecules by either creatinga reference AEDS state with more water molecules or randomly selectingthe water molecule to probe, which should also lead to the correctensemble. Probing more than a few water molecules in the active statewould become particularly relevant when computing full binding freeenergies rather than RBFE differences between compounds.31 Instead of using Monte Carlo moves to place water molecules in apredefined area, AEDS allows for constant fluctuation of predefinedputative water molecules of interest, with virtually no impact inthe performance when compared to a conventional MD of the same system.The use of AEDS waters offers both advantages and disadvantages whencompared to those methods. On one hand, AEDS is not able to placenew water molecules in the binding site, which means that a previousexamination of the system is required to decide how many hydrationsites need to be probed. On the other hand, AEDS allows for the comparisonof the likelihood of the water molecule to be in a given hydrationsite in short time scales while also quickly adapting to other molecularmotions as seen, for example, in the case of the ligand Cumming-4j.42 Additionally, since AEDS uses a boosting potentialto accelerate the free-energy landscape of the AEDS molecule withits surroundings, the frequency in which molecular events of interestare observed will also be increased.37 Overall,the use of AEDS waters has the potential to aid in the rational designof ligands by allowing to probe putative hydration sites and to becombined with other free-energy techniques.ThisAEDS water approach works in a different manner than thatof the other methods developed to deal with the water sampling issue,which makes direct comparisons difficult.


Chronic back and knee pain impairs health- related quality of life (HRQoL) and patient enablement can improve HRQoL.To determine whether enablement was a moderator of the effect of chronic back and knee pain on HRQoL.A cross-sectional study of Chinese patients with chronic back and knee problems in public primary care clinics in Hong Kong.Each participant completed the Chinese Patient Enablement Instrument-2 (PEI-2), the Chinese Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the Pain Rating Scale (PRS). Multivariable regression examined the effects of PRS score and PEI-2 score on WOMAC total score. A moderation regression model and simple slope analysis were used to evaluate whether the interaction between enablement (PEI-2) and pain (PRS) had a significant effect on HRQoL (WOMAC).P<0.001), whereas PEI-2 score was associated inversely with WOMAC total score  and PRS score. The effect of the interaction between PRS and PEI-2 (PRS × PEI-2) scores on WOMAC total score was significant  suggesting PEI-2 was a moderator. Simple slope analyses showed that the relationship between PRS and WOMAC was stronger for participants with a low level of PEI-2 (gradient 3.056) than for those with a high level of PEI-2 (gradient 1.746).Valid patient-reported outcome data from 1306 participants were analysed. PRS score was associated with WOMAC total score (β = 0.326, Patient enablement moderated the impact of pain on HRQoL. A higher level of enablement can lessen impairment in HRQoL associated with chronic back and knee pain. Illness perception focuses on how a patient experiences and mentally frames their life with illness,This study aimed to determine whether enablement was a moderator of the effect of chronic back and knee pain on HRQoL. The authors hypothesised that pain and enablement would affect HRQoL, and that the effect of pain on HRQoL could be moderated by enablement. The conceptual model is shown in Supplementary Figure S1.34 There are 73 GOPCs organised in seven clusters around Hong Kong. The care in GOPCs is standardised by evidence- based guidelines whereas care is more variable in the private sector. Participants of this study were recruited from six GOPCs in two clusters.The study was carried out in Hong Kong where the healthcare system is serviced by both the public and private sectors, and residents can freely choose where they receive care without means testing. As a result of the heavy financial subsidies, the public primary care clinics, called general outpatient clinics (GOPCs), are the main care providers for patients with chronic diseases.35 Participant inclusion criteria were adults aged ≥18 years, who had a doctor-diagnosed symptomatic back and/or knee problem that was expected to last for ≥1 month. The exclusion criteria were patients who had a life expectancy <12 months, had current cancers, were too ill, or were unable to communicate in Chinese. All eligible patients were recruited by their doctors or trained research assistants in the clinics between 1 June 2020 and 31 December 2021. After completion of a written consent form, participants’ sociodemographic and clinical data were collected by structured questionnaires and PROs by a telephone survey administered by an independent survey organisation. The participant flow chart is shown in Supplementary Figure S2. More details on participant recruitment and data collection are described in another article.35This cross-sectional study was part of a single-blinded cluster randomised controlled trial to evaluate the effectiveness of measuring and reporting HRQoL in routine clinical practice.36 showed a power of 0.95 for moderation analysis at a significance criteria of α = 0.05, 2F = 0.011.A total of 1221 participants who had complete PRO data were included in this moderation study. Post hoc power analysis, using G*Power version 3.1.9.7,Participants’ sociodemographic information, lifestyle factors, and self-reported comorbidities were collected by a questionnaire administered by a trained research assistant in the clinic. Another questionnaire was completed by the physician to collect data on the diagnosis and duration of musculoskeletal problems and to provide a global rating about the severity of the participant’s condition on a five-point Likert scale (GRS) from 1 (no problem) to 5 (very severe).et al15 and consists of six items: ability to cope with life; ability to understand one’s illness; ability to cope with one’s illness; ability to keep oneself healthy; confidence about one’s health; and ability to help oneself. Each item is rated on a 5-point Likert scale, ranging from 1  to 5 (extremely well). The item scores are summated to give a total PEI-2 score (range: 6–30), with higher scores indicating better enablement. Missing item values can be imputed with the average of the scores of answered items, up to three items. The Chinese PEI-2 is valid, reliable, and sensitive in a Chinese population.14The Chinese PEI-2 is a PRO measure that was adapted from the PEI originally developed by Howie 37 and back38 problems. It consists of 24 items in three domains: pain (five items); stiffness (two items); and physical function (17 items). Each item is rated on a 5-point Likert scale (0 to 4), with higher scores indicating more symptoms or greater impairment. The item scores in each domain are summated as the domain score. The total WOMAC score is the sum of the three domain scores, ranging from 0 to 96.39 For participants with completion of at least four of the five pain items, one of the two stiffness items, and 14 of the 17 function items, missing item values can be imputed with the average of the completed items in the relevant subscale.40 The Chinese version of WOMAC is valid, reliable, and sensitive in Chinese patients.41The WOMAC is a widely used HRQoL measure specific to musculoskeletal problems, which has been applied to patients with knee42 to assess the severity of pain. The validity and reliability of the Chinese version is established.43The PRS is a self- reported tool consisting of a scale with extreme anchors of ‘ no pain (0)’ to ‘ extreme pain (10)’Descriptive statistics were used to describe the patients’ sociodemographic information, lifestyle factors, disease characteristics, and PROs. One-way analysis of variance (ANOVA) was used to test the mean differences in the PROs between the groups of patients diagnosed with a back problem only, knee problem only, and both back and knee problems. Post hoc test (by Tukey’s honestly significant difference) was used to compare each group against the others. Pearson’s correlation between different PROs was examined. Multivariable regression was used to test the effect of disease characteristics, PRS score, and PEI-2 score on WOMAC total score, adjusted by sociodemographic characteristics.44 The PRS and PEI-2 scores were standardised (converted to a Z-score) to avoid multicollinearity in the moderation analysis. Other variables that had significant effects on WOMAC total score were included as confounders in the moderation regression model. The moderation analysis was carried out by Hayes’s PROCESS macro44 for SPSS Model 1. In addition, a simple slope analysis was used to show the moderation effect.45 The mean of the estimated WOMAC total score (Ŷ) was calculated according to the following equation:Moderation analysis was used to test if enablement (PEI-2 score as the moderator) would interact with pain level (PRS score as the predictor) to influence HRQoL . Moderation analysis hypothesises that a predictor and the interaction between a predictor and a moderator can predict the outcome if there is a ‘moderation effect’.A 5% statistical significance was used for all analyses. All the analyses were carried out with IBM SPSS Statistics (version 27).46 The mean and median GRS score was 2.45 (SD 0.64) and 2 (range 1 to 5), respectively.n = 13) (see Supplementary Table S1). Valid PEI-2 scores and PRS scores were available for 1258 and 1307 participants, respectively. Valid WOMAC scores (the primary outcome) were available from 1306 participants whose characteristics were similar to those with incomplete data  found that participants with both back and knee problems had significantly higher WOMAC scores than participants with back or knee problems only. Participants with back problems only had significantly higher PRS scores than those with knee problems only.r = 0.506, P<0.001) but negatively correlated with the PEI-2 score . The PEI-2 score was negatively correlated with the PRS score .Pearson correlation analysis  found thP<0.001), GRS score , and number of comorbidities  were positively associated with the WOMAC total score. A higher PEI-2 score was associated with a lower WOMAC total score .Multivariable regression analysis with adjustment for sociodemographic and lifestyle factors  showed tThe result of the moderation analysis is shown in Simple slope analysis  shows a As the WOMAC total score includes the pain domain that may bias the association with PRS, a sensitivity analysis was carried out on the moderation effect using the WOMAC stiffness domain and physical function domain summation score (excluding the pain domain score) as the outcome. It showed similar results , further substantiating that PEI-2 was a moderator between PRS and WOMAC total scores.This study examined the moderation of patient enablement on the negative impact of pain on HRQoL in the context of chronic back and knee problems. The results confirmed that patient self-reported pain and enablement were the most significant determinants of HRQoL. As hypothesised, a higher level of pain was associated with more impairment, and a higher level of enablement was associated with less impairment of HRQoL. This study has established that enablement was a significant moderator of the association between pain and HRQoL. Specifically, a high level of enablement weakens the effect of pain on HRQoL impairment.The strength of the study was the large number of participants recruited from multiple primary care clinics. The age and gender distribution of the participants was similar to that of the back and knee problem patient population found in the Hong Kong territory-wide primary care morbidity survey in 2021/2022. The results may be generalisable to the general Hong Kong population. The PRO data were measured with standard valid measures and they established patient enablement as a moderator of the association between pain and HRQoL. The sensitivity analyses showed consistent results, supporting reliability. There were a few limitations to this study. First, the analysis of cross-sectional data could not ascertain a causal relationship, and the correlations between PEI and WOMAC (−0.39) and PRS (−0.20) scores were relatively small. Second, the results from patients in public primary care clinics may not be applicable to those managed in the private sector. Third, self-report data are subject to bias from recall and a tendency to give socially acceptable answers. The authors believe these biases applied to all participants and should not have affected the moderation analysis results. Finally, the results from Chinese patients may not be generalisable to populations who are not Chinese.8–11,27,48–50 and the positive association between enablement and HRQoL.51 A significant, although weak, negative association was found between enablement and pain, which moderated the association between pain and HRQoL. Theoretically, enablement promotes the patient’s ability to control their health, life, and coping strategies, which can lessen pain, depression, and limitation in daily functioning; therefore, it can improve HRQoL.26,27,52 Another mechanism by which enablement helps is through improved health literacy.25 Studies in Australia have shown that patients with chronic diseases who were more enabled were more likely to have a higher level of health literacy and to actively manage their health.20 In addition, health literacy can influence an individual’s illness perception resulting in better coping ability.53The current study confirmed the findings from previous studies on the negative association between pain severity and HRQoL54,55 Education about the nature of the musculoskeletal problem and self- care strategies should be part of routine care for these patients.56 Exercise and self-care management programmes can reduce pain and improve function in patients with musculoskeletal problems.57,58 Appropriate referrals to other healthcare professionals can further enable patients to overcome specific environmental limitations. For instance, occupational therapists can advise on home modification so that patients can continue to live comfortably at home.The findings on the role of enablement as a moderator between pain and HRQoL has implications for a paradigm change in clinical practice. Most chronic musculoskeletal problems are progressive degenerative conditions with no cure. Primary care physicians should recognise patient enablement as a first-line intervention for chronic musculoskeletal problems. The scope of patient-centred care should be broadened to include an assessment of HRQoL and enablement so that management can be customised to enhance coping, health literacy, and self-care to maximise functioning. A reorientation of the treatment goal may help reduce the use of analgesics that can cause serious side effects and are subject to misuse.In conclusion, patient enablement moderated the negative impact of pain on HRQoL. Higher level of enablement can lessen the impairment of HRQoL related to chronic back and knee pain. Education, exercise, and self-care programmes that aim to enhance patients’ health literacy, functioning, and ability to cope with their illness should become routine treatments for chronic musculoskeletal problems. Future studies with more diverse populations are required to establish the interaction between enablement and pain on HRQoL, and how patient enablement can be effectively enhanced in patients with chronic musculoskeletal problems.


Moreover, elevated pretransplant serum creatinine and need for kidney replacement therapy is associated with nonresolution of HRS-1 after liver transplant.2 Treatment of HRS-1 often necessitates admission into the intensive care unit (ICU) for the administration of vasopressor support. In patients with HRS-1, the mean length of ICU stay is 10 days, with an average total hospital stay of 30.5 days.3 The diagnosis of AKI in patients with cirrhosis is independently associated with ICU admission, in addition to increased duration of hospital stay, development of multiple organ failure, and increased in-hospital mortality and 90-day mortality.4 Despite the significance of AKI in cirrhosis, several treatment questions remain, such as appropriate treatment parameters like mean arterial pressure (MAP) goals and urine output, to duration of treatment and weaning parameters, to more challenging questions such as when to consider renal replacement therapy or the effect of HRS-1 on liver transplantation evaluation, all leading to substantial resource allocation to these patients in the ICU.Hepatorenal syndrome type-1 (HRS-1) is a frequent cause of AKI in cirrhosis and is associated with significant mortality. The median survival in HRS-1 is 8–12 weeks, with a 30-day survival of 25%.Kidney360, Velez and colleagues attempt to identify factors associated with improved kidney function in patients with HRS-1 and whether differences could be observed between norepinephrine and octreotide/midodrine. Of particular interest was determining whether kidney function improved with varying MAP increases of 5, 10, or 15 mm Hg or whether fixed titration parameters  was sufficient. The retrospective cohort included 77 hospitalized patients with cirrhosis diagnosed with HRS-1 and treated with vasopressor therapy. The primary endpoint was defined as >30% reduction in serum creatinine on cessation of vasopressor support without the need for RRT or death. A significant reduction in serum creatinine was identified in patients with MAP increases of ≥15 mm Hg compared with those with increases of 5 or 10 mm Hg. No differences in serum creatinine was identified when patients were stratified by a fixed absolute parameter. The duration of vasopressor therapy needed to demonstrate evidence of therapeutic response also yielded interesting findings. Of the 25 patients who met the primary end point, 22 (88%) had improvements in serum creatinine within the first 48 hours, with 96%  by 72 hours, suggesting a fairly rapid response to vasopressor therapy in those who saw a significant improvement in kidney function.In this issue of 5 As a result, absolute parameter goals, such as a MAP of 85 mm Hg, may be difficult to achieve or require high doses of vasopressor support, whereas a targeted goal of ≥15 mm Hg is potentially a more reasonable approach. In addition, 96% of patients who experienced improvement in their kidney function demonstrated evidence within the first 72 hours of administration. This provides insight into a reasonable duration of time that can be expected to note if an improvement in kidney function is seen but also identifies an extremely at-risk population for morbidity and mortality among those who do not have any improvement within 72 hours.These findings provide useful observations regarding the treatment goals and expectations for improved kidney function in patients with HRS-1. Patients with advanced cirrhosis typically have lower MAPs and decreased systemic vascular resistance at baseline, largely attributed to imbalance of compensatory homeostatic and vasoactive mechanisms.This study does have several notable limitations. Only a third of the patients enrolled in the study met the primary study end point, limiting the studies' power to compare differences among the differing MAP parameters. In addition, drug administration was not uniformly executed as part of institutional protocols. In most instances, the goal of therapy was to achieve a rise in MAP of ≥5–15 mm Hg, an absolute MAP target of 85 mm Hg, or a range of absolute MAP of 80–85 or 85–90 mm Hg, making comparison difficult between the groups. Given the varying individual MAP response to vasopressor therapy, it is possible that the study identified a subgroup that was more responsive to vasopressor therapy and that this responsiveness provided therapeutic benefit, rather than the actual increase in MAP itself. Furthermore, the mean MAP of 70 seems high for this critically ill population, and an increase of 15 mm Hg in this instance interferes with the absolute targets that were specified. It would be interesting, for instance, to see whether an increase in MAP from 55 to 70 mm Hg was similarly efficacious.6 or terlipressin7 but remains the primary treatment outside of the ICU in the United States. Norepinephrine was the only vasopressor therapy available in the United States that had demonstrated improved kidney function in HRS8 before terlipressin was US Food and Drug Administration–approved for use in HRS in September 2022.9 Norepinephrine and terlipressin may have provided a more effective comparison, but terlipressin was notably associated with significant respiratory complications in a recent randomized trial.10 Of further note, serum creatinine is an imperfect marker for kidney function in patients with cirrhosis because it underestimates renal function due to reduced muscle mass, impaired hepatic production of creatine, and the increased tubular secretion of creatinine. The limitations of serum creatinine highlight the importance of identifying novel biomarkers of kidney function that may more accurately reflect changes in kidney function and facilitate earlier recognition of declining function before the development of HRS-1.In this study, 36% of the cohort received midodrine and octreotide, which has been shown to be less efficacious in the management of HRS-1 when compared with norepinephrineIn conclusion, the results of this retrospective cohort study provide interesting insights into the MAP parameters and duration of time that should be targeted to maximize the likelihood for improved renal function. Future considerations might include a randomized trial that can better stratify MAP parameters to overcome the potential for confounding within the study or the administration of stable doses of norepinephrine to patients on lower acuity services outside of the ICU to determine whether a similar safety profile and improvement in kidney function can be identified.