Over 5 years, 130 adult patients were treated: 79 male and 51 female with an average age of 42.3 years . Their ethnic backgrounds were; 115 Caucasian, 11 South Asian, 1 Chinese, and 3 Unspecified. On admission, the average Y-BOCS total score was 36.9 . At discharge, patients had improved on average by 36% (Y-BOCS reduction to 23.4 = moderate OCD). Similar reduction in Y-BOCS continued throughout the year with an average Y-BOCS of 22.9 at 1 month (n = 69); 23 at 3 months (n = 70); 21.3 at 6 months (n = 78) and 21.9 at 1 year (n = 77). Twenty-seven patients did not attend any follow-up appointment whilst others attended at least one appointment with the majority attending more than 3. Using student t-test, improvements at discharge, 1, 3, 6, and 12 months post-treatment showed a highly significant improvement (p < 0.001). Gains made following inpatient treatment for treatment-refractory OCD were generally maintained until 1 year post-treatment.Obsessive-compulsive disorder (OCD) generally responds to first-line treatment but patients often relapse. The United Kingdom National OCD Inpatient Service treats patients who have failed to respond to at least two trials of SRI, augmented with a dopamine blocker and two trials of ERP. Despite this, they have profound treatment-refractory OCD and require 24-h nursing care due to severe OCD. We examined patients' Y-BOCS score on admission, discharge and at each follow-up from all patients discharged over 5 years (02/01/2014-31/12/18). Data were analysed using SPSS. Paired student  Treatment for obsessive-compulsive disorder (OCD) consists of psychopharmacological agents which act on the serotonin system and psychological treatments involving exposure and response prevention (ERP). However, approximately one third of patients do not respond to first line pharmacological treatment  and despIn addition to the fact that a high proportion of people treated for OCD fail to derive benefit, there has also been evidence that up to 50% of patients relapse after treatment . On the In the UK in 2005, the National Institute for Health and Clinical Excellence (NIHCE) published guidance about the recommended treatment for OCD which sets out first line and second line treatments including psychopharmacological and psychological interventions . The appIn response to the 2005 NIHCE Guidance, the National Department of Health funded highly specialised teams to treat the most profoundly ill patients with OCD who had failed to respond to all previous treatments . In ordeScore greater than 30/40 on Y-BOCS.Have received at least two previous trials of SRIs in maximum licenced dosages for a minimum of 3 months each and without response.Have had at least one of those trials of SRIs augmented in a way recommended by Pallanti et al. This most commonly was augmentation with a dopamine blocker .Have received two trials of CBT incorporating ERP where one of these trials should have taken place in the patient's own home or in whichever environment the symptoms are maximal.Patients met the threshold for inpatient treatment if they failed to improve with the above interventions. Patients were also eligible if they were a risk to themselves because of self-neglect related to their OCD or if they had other difficulties such as urinary or faecal incontinence.The service at South West London and St George's Mental Health NHS Trust comprises the only 24 h staffed dedicated inpatient service for OCD patients in UK that is funded centrally. Data from this Service demonstrates that inpatient care is effective and patients benefit from a 40% reduction in OCD symptoms; these gains are generally maintained on average at 19 months follow-up , 13. PatEvidence suggests that the combination of medication and psychological therapy is effective in OCD and one study found a 41% reduction in total Y-BOCS score after treatment with CBT + SRI/placebo and this improvement was sustained after 6-8 years . This stIn this naturalistic study, we analysed data from the electronic medical records of all patients who had been discharged from our service in a 5 year period from 02/01/2014-31/12/18.via telephone and where necessary thereafter this was followed up by a home visit irrespective of the distance they lived from the hospital. Patients who were unable to accept inpatient treatment were offered home-based therapy with the aim of overcoming the obstacles to their accepting admission. For those patients who agreed to be admitted to the unit, all had received multiple trials of pharmacotherapy and so on admission, the response to each medication was discussed with the patient and, in collaboration with them an optimal regime was discussed. For some patients this meant remaining on the regime they had been taking already and for others switching medication.Patients who had been unable to attend the hospital for assessment had been assessed t-tests were used to evaluate improvement in Y-BOCS scores from admission to discharge and each follow up appointment and using intention to treat analyses.The basis of the therapy regime with the inpatients was a therapy session at least weekly with a therapist as well as daily sessions with the nurses. This included behavioural interventions of graded exposure with self-imposed response prevention. Although there were dedicated CBT therapists, many members of the inpatient team had qualifications in CBT and this included senior medical and some nursing staff. Daily group and individual sessions were provided by the occupational therapist. These sessions were also based on the concepts of graded exposure and response prevention and were created individually with each of the patients. Following inpatient treatment, all patients were encouraged to create their own individualised relapse prevention plan which was also shared with their local team. The National Specialist Unit routinely follows up the patients at 1 week; 1 month; 3 month; 6 months and 1 year after the inpatient stay by a member of the clinical staff either remotely or in person. The severity of OCD symptoms were measured as part of the follow up using the Y-BOCS scale. Y-BOCS total scores on admission and discharge as well as at each follow up were extracted and analysed. As patients did not attend every follow-up, numbers vary over the year. Basic demographics including age, gender and ethnicity were also extracted and data were analysed using SPSS software. Paired student Over 5 years, 130 patients were treated; 79 men and 51 women with an average age of 42.3 years . Their ethnic backgrounds were; 115 Caucasian, 11 South Asian, 1 Chinese, and 3 Unspecified.n = 69); 23 at 3 months (n = 70); 21.3 at 6 months (n = 78) and 21.9 at 1 year (n = 77) . At discharge, patients had improved on average by 36% (Y-BOCS reduction on average to 23.4 = moderate OCD). Similar reduction in Y-BOCS continued throughout the year with an average Y-BOCS of 22.9 at 1 month ((n = 77) , 2..6. At diTwenty seven patients did not attend any follow-up appointment whilst others attended at least one appointment with the majority attending more than 3. The reasons for loss to follow up were not explored in this study and most of these patients were those who had failed to derive benefit from their inpatient stay.t-test; improvements at discharge, 1, 3, 6, and 12 months all showed a highly significant improvement (p < 0.001).Using paired This study adds to the existing evidence of the longer term impact of combined treatment (medication with CBT/ERP) in patients with treatment-refractory OCD Patients with profound treatment-refractory OCD had a 36% reduction in their Y-BOCS scores following inpatient treatment. This improvement from admission to discharge reflects an improvement from profound treatment-refractory OCD to moderate OCD. This improvement is sustained over the first year post-discharge from the ward with minimal fluctuations in their Y-BOCS scores during this period.Previous studies have explored the impact of either pharmacological or psychological treatments on OCD. During their inpatient stay our patients received a combination of both psychopharmacology which was optimised on admission and CBT with ERP and we assessed the longer term (over 1 year) impact of this combined treatment approach. Many studies have assessed the impact of OCD treatment in outpatients with moderate-severe OCD. Similar studies conducted on both inpatient and outpatient samples have demonstrated relapse estimates of up to 50% . Our finOur service provides individualised treatment programmes including medicine optimisation, creating individualised exposure programmes combined with group occupational therapy sessions focussing on facing up to fear and activities of daily living. As such this could be replicated elsewhere. The importance of a dedicated service where patients can advise, inspire and help each other should not be underestimated.In our study, patient adherence to follow up appointments was good with 60% attending three or more appointments after discharge. However, we were unable to gather follow up data for ~20% of the patients we treated over the 5-year period. A further study exploring reasons for lack of follow up in this subgroup of patients would be beneficial to improve our understanding of the longer term impact of inpatient OCD treatment.This study did not take into account other measures of OCD not listed in the Y-BOCS or other factors influencing a patient's recovery such as time spent in inpatient service, quality of life, comorbid mental health problems or use of other psychotropic medication. It is unusual for a patient admitted to the service not to also have clinical signs of depression and previous studies from this unit have demonstrated that almost 80% of patients admitted had clinical evidence of moderate or severe depression as indicated by the Beck Depression Inventory . A retroThis was a naturalistic study analysing data from patients with profound treatment-refractory OCD and this is one of the few studies to date of long term follow up post discharge after inpatient OCD treatment. This information fills an important knowledge gap in the literature.All patients admitted to and discharged from the unit over a 5 year period were followed up for 1 year post discharge providing data over a significant time period which is difficult to collect.This provides a robust sample size of 130 patients who were all treated at the same centre which adds to the validity of the results.The same outcome measure  was used at each follow up to ensure that the data is comparable with minimal confounding variables.Data was sourced from patients' electronic records ensuring the use of accurate and reliable information as documented by clinicians.Although this study explored long term patient outcome after inpatient treatment, only the Y-BOCS score was used to determine patients' symptom severity. No other follow-up data was assessed. This study was not designed to assess other measures and data was based on convenience sampling of existing clinical data.A minority of patients were lost to follow up due to unknown reasons. Further information related to this would be important as well as ongoing medication use.Any comorbidities patients had were not assessed or monitored in this study.This study demonstrates that a majority of patients with the most profound refractory OCD are able to maintain the gains made for at least 1 year after treatment with optimised psychopharmacology as well as CBT with ERP. It would be beneficial to further study whether the benefit of treatment continues to be sustained after 1 year post discharge.This study demonstrates evidence that the gains made following inpatient treatment for treatment-refractory OCD are generally maintained until 1 year post-treatment.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.NN and LD designed the study, analysed the results, and wrote the paper. EC completed data extraction. All authors contributed to the article and approved the submitted version.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. The handling editor declared a past co-authorship with one of the authors LD.


Our study assesses the relationship between the exposure of youth to the US Food and Drug Administration’s national tobacco public education campaign, The Real Cost, and changes in campaign-focused risk perceptions and beliefs.A nationally representative cohort study of youth was conducted from June 2018 to July 2019, consisting of a baseline and one follow-up survey. We performed logistic regressions to examine the association between campaign exposure and beliefs. Exposure was measured by self-report as the frequency of exposure to individual campaign advertisements about the health consequences of e-cigarette use and of smoking cigarettes.We found that increased levels of exposure to campaign advertising was associated with a significant increase in the odds of reporting agreement with campaign-specific beliefs. Positive patterns of findings were found across multiple items selected by specific advertisements, whereas unrelated beliefs were not associated with advertisement exposure.A sustained national tobacco public education campaign can change beliefs about the harms of e-cigarette use and cigarette smoking among youth. Combined with other findings from The Real Cost evaluation, results indicate that prevention mass media campaigns continue to be an effective and cost-efficient approach to reduce the health and financial cost of tobacco use in the US. Public health educational campaigns that focus on smoking prevention among youth can influence their beliefs and risk perceptions about the harms of cigarettes as a precursor to behavior change.Public health educational campaigns can continue to be effective at shifting beliefs for low use of products, such as cigarettes. These campaigns can also shift beliefs and risk perceptions about the harms of a novel product such as e-cigarettes. Advertisements that focus on the harms of nicotine may be effective in shifting beliefs about the harms of multiple tobacco products.Our study supports sustained funding for health education that focuses on preventing use of a variety of tobacco products among youth.Every year since 2011, cigarette use among youth in the US has decreased, with historic lows in recent years . In 2019Effective national tobacco control strategies employ a combination of efforts — public education campaigns, smoke-free laws, taxes, and graphic warning labels among other measures — to reduce smoking prevalence in the population ,7. PolicIncreases in youth risk perceptions and agreement with beliefs about the harms of cigarettes and e-cigarettes indicate progress toward reductions in use and initiation. However, perceptions and beliefs can be affected by external influences, such as new stories about the risk of e-cigarettes, and other respondent characteristics, such as age. Thus, it is important to demonstrate a relationship between exposure and agreement with campaign-specific beliefs to serve as an early indicator of campaign effectiveness. We used data from the first 2 waves of a national longitudinal survey of US youth that took place during 2018 and 2019 to 1) examine changes in cigarette and e-cigarette beliefs and 2) examine association between self-reported exposure to The Real Cost advertisements (ads) and participant agreement with corresponding campaign-specific beliefs.Development of The Real Cost campaign was based on health behavior change theories, such as a reasoned action approach to behavior prediction, positing that exposure to the campaign leads to changes in tobacco-related beliefs –13, whicDuring this study, The Real Cost focused on health consequences associated with cigarette use, like loss of teeth (the Gift ad) and reduced lung development (the Little Lungs ad). From 2017 to 2019, the cigarette ads in this evaluation were placed on television, digital video, display banners, social media, online radio, and a website. In September 2017, FDA placed the e-cigarette ad called Rehacked online. Rehacked was originally created as a cigarette use prevention ad with the message that nicotine addiction can hack your brain. The ad was modified to message on e-cigarette use to address the rising use rates among youth. FDA began creating additional e-cigarette ads under The Real Cost e-cigarette campaign and announced the launch in September 2018 . Like thConducted as part of The Real Cost evaluation, data are from a nationally representative longitudinal survey of US youth aged 11 to 16 years at baseline. We used an address-based sampling frame to randomly draw households clustered in 100 Census Public Use Microdata Areas (PUMAs) and supplemented the frame with market research databases to identify households likely to have at least 1 eligible young person (approximately 5% of households). A letter describing the study was mailed to each of the selected addresses. Subsequently, a field interviewer visited each address to secure an immediate interview or schedule one for a later date. In-person baseline data collection took place from June to October 2018. First follow-up data collection (April to July 2019) consisted of online or in-person interviews with participants after obtaining parental permission and participant assent. Participants received a $20 incentive for completion of the baseline survey and $20 or $25 for completion of the first follow-up survey. At baseline, 2,847 sampled households were eligible to participate . An additional 1,715 households with unknown eligibility were estimated to be eligible. A total of 4,039 young people completed the baseline survey. The unweighted household-level response rate was 61.5%, based on the American Association of Public Opinion Research Response Rate number 4 formula  populatiThe final total sample size for the first follow-up survey was 3,354 youth, which was the sample used for this study’s analyses. The study was approved by institutional review boards at the FDA and RTI International.Campaign-specific beliefs are a standard outcome measure of effectiveness for public education campaigns ,22–25. TThe independent variable of interest in the models was self-reported exposure to campaign advertisements. At each wave, respondents were shown campaign ads and asked to assess their frequency of exposure. To measure confirmed advertisement recall, as used in numerous evaluation studies of antismoking campaigns , the queModels controlled for factors that influence susceptibility to tobacco use and risky behaviors. Factors include the Brief Sensation Seeking Scale (BSSS-4) (Cronbach α = 0.77). Statements used were 1) I would like to explore strange places, 2) I like to do frightening things, 3) I like new and exciting experiences, even if I have to break the rules, and 4) I prefer friends who are exciting and unpredictable. Responses ranged from 1 (disagree strongly) to 5 (agree strongly). Educational plans were assessed by using the question, “How far do you think you will go in school?” Responses ranged from 1 (I don’t plan to go to school anymore) to 8 . School environment was measured as the mean of responses to 3 statements (α = 0.80): 1) I feel close to people at my school, 2) I am happy to be at my school, and 3) I feel like I am a part of my school. Responses ranged from 1 (disagree strongly) to 5 (agree strongly). School performance was assessed with the question asking “How well would you say you have done in school?” Responses ranged from 1 (much worse than average) to 5 (much better than average). Parent communication was a mean of 2 items (α = 0.73): 1) Thinking about the adult or adults you live with, would you say you are satisfied with the way you communicate with each other? and 2) How close do you feel to the adult or adults you live with? Responses for the first item ranged from 1 (very unsatisfied) to 5 (very satisfied). Responses for the second item ranged from 1  to 5 (very close). Negative parent interaction was assessed as 1 item, “How often has a parent or other adult caregiver said things that really hurt your feelings or made you feel like you were not wanted or loved?” Responses ranged from 1 (this has never happened) to 6 (>10 times). Religiosity was assessed with the item, “How often do you attend church or religious services?” Responses ranged from 1 (never) to 6 (>1 weekly). Self-reported awareness of 2 other national campaigns, Tips from Former Smokers (Centers for Disease Control and Prevention) and “truth” (Truth Initiative) were assessed by using the item, “In the past 3 months, have you seen or heard the following slogan or theme [campaign]?” Response choices were yes or no. Media use was assessed as a 3-level categorical variable , using tertiles for 2 variables, daily hours of television use and daily hours of social media use.Baseline individual characteristics as controls in the analytic models included indicators for the following: age, female refence, Black, Hispanic, and other non-Hispanic races and ethnicities refence, the presence of a tobacco user in the household (cigarette beliefs) or e-cigarette user in the household (e-cigarette beliefs), the presence of a household smoking ban (cigarette beliefs) or a household e-cigarette ban (e-cigarette beliefs), and a continuous measure of weekly income for young person(s). The cigarette belief models included state adult smoking prevalence from the 2018 Behavioral Risk Factor Surveillance System (BRFSS), measured in percentage points. Because of lack of available e-cigarette use prevalence data in the 2018 BRFSS, the e-cigarette belief model included adult e-cigarette use prevalence from the 2017 BRFSS measured in percentage points.t tests to assess changes in agreement with beliefs between baseline and the first follow-up. We then used multivariable logistic regression to estimate the odds of agreement with each campaign-specific belief as a function of self-reported exposure to the ad that relayed the belief. Models controlled for demographic and environmental characteristics and 3 media market variables: 1) median population size (in tens of thousands), 2) median income (in tens of thousands of dollars), and 3) media market education level (the proportion with a bachelor’s degree or higher). Models also included a wave indicator to control for the average difference in the outcome between waves. The models were estimated by using Stata’s xtlogit command using complete case analysis (StataCorp LLC). To interpret results, we used Stata’s post-estimation margins command to look at the predicted agreement with beliefs by exposure level.We used descriptive statistics to summarize the sample characteristics and self-reported exposure to The Real Cost. We used descriptive statistics to summarize agreement with beliefs and used Participants were aged 11 to 16 at baseline, with an even distribution across individual ages. The sample was divided evenly by sex, and most participants (51.9%) were White .Self-reported awareness of The Real Cost advertisements was assessed for 4 ads: 2 e-cigarette ads and 2 cigarette ads . At baseWe assessed changes over time for beliefs. Percentage agreement with 15 of the 17 ad-specific e-cigarette beliefs significantly increased between baseline and first follow-up. Agreement with 5 of the 13 ad-specific cigarette beliefs significantly increased between baseline and first follow-up. On average, agreement with the ad-specific e-cigarette beliefs was lower at baseline than agreement with the ad-specific cigarette beliefs .P < .001). Exposure to The Real Cost was also significantly associated with an increase in the odds of agreement with 8 of the 13 campaign-specific cigarette beliefs, 3 of the 5 beliefs relayed by the ad Little Lungs and 3 of the 6 beliefs relayed by the Gift ad. Additionally, exposure to the Rehacked e-cigarette ad was significantly associated with an increase in the odds of agreeing with 2 cigarette-related beliefs around nicotine. Unadjusted model results where beliefs were modeled only as a function of self-reported exposure were consistent with the adjusted model results. No consistent patterns of significance were observed between exposure to The Real Cost and unrelated beliefs. Exposure to The Real Cost e-cigarette ads was associated with an increase in the odds of agreement with 1 of the 6 unrelated e-cigarette beliefs, and exposure to The Real Cost cigarette ads was not associated with agreement for any of the 5 unrelated cigarette beliefs.Increases in exposure to The Real Cost campaign ads were significantly associated with an increase in the odds of agreement with 6 of the 17 campaign-specific e-cigarette beliefs, 3 of the 8 beliefs from the ad Rehacked and 3 of the 9 beliefs relayed by the Epidemic ad. For example, a 1-unit increase in exposure to the ad Rehacked resulted in an 18% increase in the odds of agreeing with the belief that the nicotine in vapes may hack your brain (adjusted odds ratio [aOR], 1.18, The predicted levels of agreement with beliefs at varying levels of self-reported exposure were assessed . The sloBecause we found little evidence to support significant effects of ad exposure on beliefs not specifically communicated by the campaign, this study suggests that results are unique to campaign-related beliefs. These are important findings given health behavior theories that posit belief change is a precursor to behavior change.We also found increased agreement with cigarette beliefs about nicotine addiction among young people who were exposed to the e-cigarette advertisement, Rehacked. Although it originated as an ad about the dangers of addictive nicotine in cigarettes, the ad was edited to replace the visual of a cigarette with an e-cigarette. The e-cigarette ad aired for approximately 1 year, from before completion of the baseline data collection through the follow-up. The cigarette ad did not air during this time. This suggests that messaging around nicotine addiction may effectively change perceptions around multiple tobacco products, regardless of the specific products displayed in the ad. As more tobacco products come on the market, public health practitioners will face increasing challenges to address the most popular products among young people. However, messaging on the shared health consequences of using these products, such as nicotine addiction, may be an effective strategy to prevent the use of multiple tobacco products.Baseline agreement with cigarette beliefs were higher than beliefs about e-cigarettes. This finding was unsurprising given the long history of prevention messages on the dangers of cigarette use, while e-cigarette prevention messaging is more recent. Despite the high baseline levels of agreement with cigarette beliefs, along with historic declines in adolescent cigarette use, findings from this study demonstrate that campaigns remain an effective tool for changing perceptions around cigarette use. Dono et al  showed tFinally, our study showed that campaign effects occurred for ads in a digital-only campaign that aired in an increasingly fragmented media environment . With inFindings from our study should be interpreted within the context of a few limitations. As mentioned in previous evaluations of The Real Cost, a longitudinal cohort does not account for maturation effects, but it is likely that, as young people age, their pro-tobacco attitudes also increase . HoweverOur study provides early evidence of the effectiveness of one of the first national youth-centered e-cigarette public education campaigns and evidence of the continued effectiveness of cigarette use prevention under the same campaign brand. Findings also provide some insights into using messages about nicotine addiction to address use of multiple tobacco products among young people. Furthermore, we found evidence of an association between exposure to a prevention campaign and increasing risk perceptions of cigarettes among youth, even within a low-prevalence environment. Future research could inform the development of tobacco use prevention campaigns that can effectively change behavior with regard to rapidly changing tobacco products and digital media landscapes.


E. coli dihydrofolate reductase (DHFR). We identified a novel compound (CD15-3) that inhibits WT DHFR and its TMP resistant variants L28R, P21L and A26T with IC50 50–75 µM against WT and TMP-resistant strains. Resistance to CD15-3 was dramatically delayed compared to TMP in in vitro evolution. Whole genome sequencing of CD15-3-resistant strains showed no mutations in the target folA locus. Rather, gene duplication of several efflux pumps gave rise to weak (about twofold increase in IC50) resistance against CD15-3. Altogether, our results demonstrate the promise of strategy to develop evolution drugs - compounds which constrain evolutionary escape routes in pathogens.Antibiotic resistance is a worldwide challenge. A potential approach to block resistance is to simultaneously inhibit WT and known escape variants of the target bacterial protein. Here, we applied an integrated computational and experimental approach to discover compounds that inhibit both WT and trimethoprim (TMP) resistant mutants of  Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.)   call forer spp.) . Apart fer spp.) . This maer spp.) . Evolutier spp.) . Developtressors . Recent tressors . Using eSelectively targeting bacterial proteins which are critical to essential bacterial life processes like cell wall biosynthesis, translation, DNA replication etc. with novel compounds forms the basis of antibiotics development programs. Dihydrofolate reductase (DHFR) is one such protein, which, due to its critical role in nucleotide biosynthesis, has been a central antibacterial and anticancer drug target . Based oE. coli clinical isolates showed the contribution of dfrB genes toward clinical TMP resistance , we selected four representative crystal structures , we were able to recover the largest proportion of known inhibitors (Table 2 and RX3) was adopted as the most representative crystal structure for the initial SBVS of compound databases for novel broad range inhibitors. It was also used for subsequent in-depth evaluation of most promising candidates using molecular dynamics simulation. The detailed discussion of the rationale behind selection of closed conformation is provided in the Materials and methods: Crystal structure selection.The key objective of our approach is to find compounds that  of five  filter t of five . First, Construction of binding affinity prediction model) to predict binding free energies calculated by MMPBSA.A total of 307 candidate compounds with strongest docking score that form hydrogen bond with the critical residue Asp27 in the DHFR-binding pocket, were submitted for more accurate prediction of binding free energy . Our appL. grayi) and Chlamydia muridarum (C. muridarum) again showing highly significant correlation between predicted and experimental values  against both the WT and all TMP-resistant variants of DHFR were selected for further evaluation. Out of this set, we selected the novel compounds that differ substantially from 183 known DHFR inhibitors . Generally, compounds with similar properties tend to have similar activity (Compound Information). Further details on SBVS can be found in the Method and Materials: Selection of virtual screening hits.Further, the analysis of the DHFR crystal structures showed that Asp27 forms hydrogen bond with almost all DHFR inhibitors in the ligand-binding cavity. Thus, compounds predicted by docking that make hydrogen bond with Asp27  and haviactivity . Based oactivity  and protactivity  of the pactivity . On the activity  with theA spectrophotometric assay  was employed to evaluate possible inhibition of catalytic activity of the 40 selected compounds against WT DHFR and its TMP resistant mutants including P21L, A26T, and L28R . All 40 50 values and converted them into corresponding Ki values  for compounds that are similar to CD15 and CD17. The search yielded a total of 12 extra compounds which were subsequently obtained and evaluated for their inhibitory activity against DHFR in vitro and as inhibitors of E. coli bacterial growth. The inhibitory 50 values compared to that of CD15 (E. coli variant strain (E. coli DHFR by CD15-3 and lack of inhibitory activity against human DHFR (E. coli DHFR (PDB 1RX3) and human DHFR (PDB 1KMS), we noted there are significant sequence differences between them in the ligand-binding domain   and clogP of CD15-3 are likely to be responsible for its superior efficacy of the bacterial growth inhibition on WT and mutant strains than other CD15 series compounds . The IC5/PAINS/) . All sixE. coli DHFR (PDB 1RX3). As shown in The binding mode of the two most promising compounds CD15 and CD15-3 were evaluated using molecular docking . BW27783 cells constitutively express arabinose transporters providing rather homogeneous response from the cell pool under arabinose induction  with D27F variant under the pBAD-promoter and the expression was induced by 0.005% external arabinose . We founThis result showed that inhibition of DHFR in vivo was, at least partially, responsible for inhibition of cellular growth induced by CD15-3. As TMP is a known inhibitor of DHFR (WT), we wanted to check if these overexpression plasmid systems behave in a similar way as in the case of CD15-3 inhibition. Overexpression of WT DHFR rescued growth of TMP-treated cells , while nE. coli cells were grown in M9 media supplemented with ‘folA mix’ in presence and absence of CD15-3. We found that growth of CD15-3-treated cells was partially rescued by folA mix and again the effect was more prominent at relatively lower concentrations of CD15-3 . A few isolated colonies were found under these conditions. We sequenced folA locus in six isolated colonies from each of CD15-3 and TMP plates and their IC50 values were measured. None of the colonies that grew on CD15-3 plates showed mutation in the folA locus  family transporters refer to a category of bacterial efflux pumps primarily observed in Gram-negative bacteria. They are located in the membrane and actively transport substrates. Cus-efflux system was initially identified for the extrusion of silver (Ag+) and copper (Cu+). They have been found to induce resistance to fosfomycin , dinitrophenol, dinitrobenzene, and ethionamide  and binds to dihydropteroate synthetase. Under both treatment conditions, CD15-3-evolved cells partially escaped the drug inhibition and showed about threefold higher IC50 for TMP and Sulphamethoxazole  . Intereshoxazole .These render TMP .E. coli cells in absence of CD15-3 (control) and with added CD15-3. Experiments were performed at 42°C. Cells were grown for 4 hours before image acquisition. DIC imaging results of the CD15-3-treated WT cells  and Life Chemicals  were selected for virtual screening. Both databases contain a large amount of diverse structures which are useful as potential hits for drug development and providing integrative drug discovery strategies for pharmaceutical and biotech companies (E. coli. DHFR inhibitors (183 compounds) with IC50 values ranging from 1 nM to 100 μM were downloaded from BindingDB (https://www.bindingdb.org/bind/index.jsp) Two commercially available databases ChemDiv  were downloaded from Protein Data Bank (PDB) (RX3 (M20 loop closed), 1RA3 (M20 loop open), 1RC4 and 5CCC (M20 loop occluded) were selected for molecular docking and molecular dynamics studies. All these protein structures were prepared by the Protein preparation wizard in Maestro 10.2 of Schrodinger  of the receptors with partial atomic charge less than the specified cutoff (set to 0.25 Å). Due to its excellent performance through a self-docking analysis (All nk (PDB)  and clasrodinger  to add hanalysis , the Gli–1 nm–1. After a primary constraint NVT simulation of 200 ps with protein atoms being fixed at 100 K, an NVT simulation of 400 ps without restraints was performed with simulated annealing at the temperature going from 100 to 300 K linearly. Then we performed an NPT simulation of 500 ps to equilibrate the pressure. Eventually, a production MD was conducted for 10 ns at 300 K twice. Bond lengths were constrained using LINCS algorithm during the simulation  are also shown. It can be seen that M20 loop constitutes part of the substrate binding site, so it will influence the binding of the substrates and its inhibitors. It has shown that the closed conformation is adopted when the substrate and cofactor are both bound , and occluded M20 1RC4 (1) and 5CCC (13). In particular, when the cutoff of docking score was set to –9, using closed M20 1RX3 as target resulted in correct prediction of as many as 62 inhibitors compared to the open M20 1RA3 (25), and occluded M20 1RC4 (4) and 5CCC (32). At the higher cutoffs, open M20 1RA3 and occluded M20 5CCC showed comparable trends with that of closed M20 1RX3. However, even the M20 loop of 1RC4 and 5CCC both belong to the occluded conformation, their performance in docking of known ligands was strikingly different. It appears that 1RC4 performed much worse than 5CCC and it performed the worst compared to other target conformations. A possible reason could be that M20 loop in 1RC4 structure is closer to the substrate, making the binding pocket smaller to accommodate relatively large inhibitors. Further, we focused on the inhibitors with IC50 values less than 1 μM. As seen from RX3 predicted about 58 % (42) out of the total of 72 the inhibitors, while the open M20 1RA3 only predicted 18 % (13), the occluded 1RC4 got 3 % (2) and the occluded 5CCC predicted about 18 % (13). Altogether these results demonstrate that the closed M20 1RX3 is more representative as a target for molecular docking of E. coli DHFR inhibitors.Through visual inspection of M20 loop conformation for the 61 crystal structures of DHFR (before 2016-9-26), crystal structures of three categories of M20 loops were obtained. From th bound , where tth bound  and alsoth bound . Observeth bound . The opeth bound . HoweverE. coli DHFR. Eight known E. coli DHFR inhibitors (RX3 and Cmpd 2 and 6 were docked to 3QYL). We used the available crystal structures of TMP complexed with Staphylococcus aureus DHFR  and sequence similarity: 92/162 (56.8%)) as a reference and found that the conformation of TMP docked with E. coli DHFR is only about 0.4 Å different from crystal structure with S. aureus DHFR (R = 0.942) between the computational and experimental binding free energies  can quickly select compounds with reasonable binding patterns and higher predicted scores from a large number of compounds. According to the workflow , resultiBased on the principle that compounds with similar properties tend to have similar activity , and to In this equation, individual fingerprint bits set in molecules A and B are represented by a and b, respectively; and c is the intersection set. eraction  were caleraction , an exteE. coli, C. muridarum and L. grayi DHFR as well as mutant E. coli DHFR proteins including P21L, A26T, and L28R point mutants were expressed and purified in the same way as previously described  escribed . Human Described . The curequation  from plod before  and [S] d before .50 values were determined. The reported IC50 values are an average based on at least three replicates and standard errors are indicated.Cultures in M9 minimal medium was grown overnight at 37°C and were then normalized to an OD of 0.1 using fresh medium. A new normalization to an OD = 0.1 was conducted after additional growth during 5–6 hr. Then, the M9 medium and six different concentrations of the positive control TMP and all hit compounds in the 96-well plates (1/5 dilution) were incubated. The incubation of the plates was performed at 37°C and the orbital shacking and absorbance measurements at 600 nm were taken every 30 min during 15 hr. By integration of the area under the growth curve (OD vs. time), the growth was quantified between 0 and 15 hr, as described elsewhere . For theE. coli K-12 substr. BW25113 reference genome (GenBank accession no. CP009273.1).Using single isolated colonies, whole genome sequencing for the evolved variants was performed resorting to Illumina MiSeq 2 × 150 bp paired-end configuration (Novogene). We used breqseq pipeline , on defaE. coli strains in the presence of prospective compound CD15-3 or TMP was carried out by serial passaging using an automated liquid handling system (Tecan Freedom Evo 150), a procedure similar to the one described previously . It is to be noted that six well plates were used for this study and the plated were incubated at 37°C for colonies to appear.For plate assays, WT cells were grown for around 8 hr without any antibiotic selection. Cells were grown in M9 media supplemented with 0.8 g L–1 glucose and casamino acids  in absence and presence of CD15-3 at 42°C for incubation and 300 rpm constant shaking. Drop in DHFR activity has been associated with cellular filamentation and similar phenotype is observed under TMP treatment  imaging using Zeis Discovery imaging workstation. Multiple fields were observed and scanned for a single condition type and a minimum of three replicates were used for imaging studies. Similar methods for imaging were used for evolved cell types under conditions of absence and presence of CD15-3 compound. Intellesis Module was used for analyzing DIC images. On average, around 500 cells were analyzed for computing cell length.  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Antibiotic resistance evolves rapidly in response to treatment. Resistance sometimes evolves predictably through the accumulation of specific mutations. One way to limit evolution is to develop other molecular compounds that target the WT protein and that remain efficient against these resistance mutations while exploiting the deep knowledge of the traditionally targeted pathways. Here, the authors use this strategy to identify potential compounds that help delay the evolution of antibiotic resistance in bacteria using a combination of computational, biochemical and experimental evolution approaches.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by George Perry as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Development of evolution drugs -antibacterial compounds that block pathways to resistance" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:E. coli, the known target of the common antibiotic trimethoprim. Contrary to trimethoprim, a new lead appears to lead very slowly to resistance. The authors present their strategy and findings as a way to develop 'evolution drugs'.Antibiotic resistance can evolve rapidly and thus reduces the usefulness of many drugs that are otherwise effective. The development of new drugs that could slow down adaptation to antibiotics would significantly extend the life expectancy of current treatment strategies. Here, the authors identify, through computational analyses and experimental work, new chemical compounds that target the dihydrofolate reductase of Essential revisions:The reviewers raised many points that would need to be addressed for the manuscript to be considered further. I outline here the critical elements that are essential to address.1) The new compounds are presented as new antibiotics in the context of medical application but they are active against the human DHFR and thus lack selectivity, which is a major issue in antibiotic development. The reviewers agree that because of this, this work could not be published under the theme of inhibitor development. It could potentially be rewritten as a model system for the examination strategies to delay the onset of resistance.2) The number of mutants sequenced is not large enough to rule out the possibility that resistance can emerge through mutations in folA. In addition, some of the mutants come from the same adapted populations, which means that they are not an independent sampling of resistance mutants. The claim that resistance mechanisms are different from that of Trimethoprim and do not involve folA itself is therefore not supported.3) Some of the information that would have been useful for the interpretation and evaluation of the current manuscript is to be published in an upcoming publication. It would be important for the reviewers to have access to this information. This would be possible for instance by providing a link to a preprint with this other paper.4) The concept of evolution drug needs to be better defined to make sure the reader understands its scope and what it is bringing to the field.We also transmit the detailed comments below.Reviewer #1:I cannot comment on the sections of the manuscript related to compound search and identification.My general comment is about what makes an evolution drug exactly. From the definition given, it is a drug that works on mutants that are resistant to other drugs. Most antibiotics that interact slightly differently with the same target, or that have other modes of action altogether would therefore be evolution drugs? Since this is the aspect of the paper that makes it very original, I would need to better understand what this concept is, what it is not, and what novelty it brings.My second critical comment regards the evolution experiment. I could not find exactly how many independent lines were evolved to resist the novel compound and how many were sequenced. The methods mention evolution in 96 well plates but from the results, it seems that only two mutants were sequenced. I believe that this is not enough to claim that this drug will not select for mutations in fola. To be able to show this, enough independent clones have to be sampled and sequenced to show that the space of resistance mutations has been completely sampled.My third critical comment is that it seems that CD15-3 selects for mechanisms that lead to resistance to TMP as well, which is not something that you want for an evolution drug. This means that resistance to CD15-3 brings resistance to TMP as well if I understand well. How does this fit within the concept of evolution drugs? This would seem to make it a rather inefficient one.Detailed comments: Panels of Figure 7 would need to be better labelled.Many barplots are used to show estimates, for instance in figure 4. This is a representation that poorly represents the data. . I recommend alternatives are used and all data points are shown in addition to error bars (which are not defined here).Reviewer #2:The authors present a computational workflow that convincingly demonstrates the design and optimization of inhibitors to a key antibiotic target, such that the development of inhibitor resistance is clearly delayed. The experimental determination of inhibition in vitro, in cells and during the course of one month under evolutionary pressure with the new compounds was convincing. Unfortunately, the new inhibitors inhibit human DHFR more strongly. They would be toxic/lethal.The authors could examine the inclusion of negative design (against inhibition of the human enzyme) in their workflow.E. coli  making DHFR an appealing target to develop evolution antibiotic drugs. » This view is now known to be incomplete. Crucially, the folA-related DfrA enzyme family should be introduced  because clinical TMP resistance is more complex than point mutations to FolA as described by Huovinen. The intrinsically TMP-resistant DfrB family of DHFRs should also be mentioned as being part of the problem, although this work would not directly apply to it.Since trimethoprim resistance is at the center of this research, and that the authors aim to develop an "evolution drug", TMP-specific resistance should be described in greater depth. The specific context and reference given in the 2nd/3rd paragraphs of the introduction are: "However, due to rapid emergence of resistant mutations in DHFR, the development of drug resistance to antifolate antibiotics belonging to any of the above-mentioned classes presents a significant challenge . Both clinical and in vitro studies have shown that accumulation of point mutations in critical amino acids residues of the binding cavity represent an important mode of trimethoprim resistance. Mutations conferring resistance in bacteria to anti-DHFR compounds are primarily located in the folA locus that encodes DHFR in As a result of the new finding that TMP resistance is very diverse and does not result only from the recent evolution of point mutations as was previously thought, the approach presented here should be qualified. In the introduction (3rd paragraph): "In this work, we developed an integrative computational modeling and biological evaluation workflow to discover novel DHFR inhibitors that are active against WT and resistant variants. » should qualify the resistant variants as being specifically directed to the point-mutated variants only, not all resistant variants that are genetically diverse.It thus follows that the premise, as currently stated, is somewhat misleading. The introduction discusses clinical observation of antibiotic evolution. However, the point mutations under study are not particularly relevant to the natural evolution of TMP resistance (prior to introduction of TMP), or even its clinical evolution. This is clearly shown by the modest TMP resistance offered . As described under Methods, section "Construction of binding affinity prediction model": "An evolutionary study  of TMP resistance found that three key resistance mutations P21L, A26T, L28R, and their combinations constitute a set that recurrently occurred in two out of five independent evolution experiments, and their order of fixation in both cases was similar." This set of mutations constitutes a simple model system of in vitro evolution. This does not remove from the success of the inhibitor discovery and delay of onset of resistance, but it must be made clear that the premise is limited to in vitro evolution as a model of natural evolution, from the Abstract and throughout the body of the work.Listeria grayi (L. grayi) and Chlamydia muridarum (C. muridarum) again showing highly significant correlation between predicted and experimental values , demonstrating broad predictive power of the method". To highlight the relevance of the study, the choice of Listeria grayi and Chlamydia muridarum should be rationalized. The native resistance of L. grayi should be expressed; the choice of C. muridarum is not so clear. Importantly, what is their evolutionary distance with E. coli FolA? Therefore, do they demonstrate broad predictive power, as stated, and does it suggest that they are broadly efficient potential antibacterial leads ?Following Figure 1: "…The authors chose to characterize the in vivo IC50 of the compounds. Since the role of compounds is to inhibit bacterial growth completely, minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) might be more relevant in this context and could be determined for the most interesting compounds, and different targets.Suggestions and typographical:– A recent review on 'classical' searches for inhibitors could be cited in the 2nd paragraph of the introduction: Wróbel, Arciszewska, Maliszewski and Drozdowska The Journal of Antibiotics volume 73, pages5-27(2020)– Confusion in describing the activity test in vitro: in the body the authors describe fluorescence at 340 nm  whereas Methods describe the decrease in absorbance at 340 nm. Figure 2A: Which is the curve of TMP?– Table 1: significant digits on values are not uniform – should be two.E. coli and in vitro are not routinely italicized.– – CD15-3 or CD15.3.– Table 1: Ki of TMP should be included for comparative purposes.– Table 2: IC50 values should have no more than 2 significant digits. TMP should have STD.– Figure 7C: Concentration in µM, not µm.E. coli variants presented in panel E. In each DIC image, there should be a size bar (ex. 5 µm).– Figure 9. The order of the panels should be the same as the order of the Reviewer #3:In this manuscript, the authors present a strategy to identify new antibiotic compounds that block DHFR -- the target of trimethoprim -- in a way that should still work in DHFR variants carrying common trimethoprim resistance mutations. Their strategy consists of a computational approach to select promising candidates from known databases, followed by an experimental optimization and validation of the identified compounds. Interestingly, the first computational steps already narrowed down large chemical databases into a few relevant compounds, which are then shown to be efficient DHFR inhibitors in the experimental tests. Moreover, evolution of resistance to the resulting compound is shown to be much slower than resistance to trimethoprim.The authors should address the following points (roughly in order of importance):– An upcoming publication, in which a second molecular target of compound CD15-3 is revealed, is mentioned several times. It is slightly frustrating that these results, which may be key to understanding some of the phenomena reported in the present work (see below), are not included in this work. Why can this information not be included in the current article?– Apart from non-synonymous mutations in folA, a common alternative way to evolve trimethoprim resistance is to overexpress DHFR. This happens in typical evolution experiments and in the clinic, for example through mutations that affect the folA promoter ; this could also happen via duplication (or further amplification) of the folA gene. Figure 6 shows that, indeed, for the CD15-3 compound, overexpressing DHFR would actually allow the cell to recover from the effect of the compound. It is thus quite striking that no overexpression of DHFR seems to occur in the evolution experiments shown in figure 7A, where no mutation in folA fixes. The authors should comment on this in more detail. Could the other (mysterious) target of the drug explain this?– How does the absolute value of the IC50 of the new compound compare with that of trimethoprim?– The manuscript has a considerable amount of technical details in the main text, which makes it unnecessarily long and sometimes difficult to follow the main ideas. A shorter main text with fewer figures would considerably increase the clarity and accessibility of this work for non-experts (the details could be shifted to a supplement or appendix).– In figure 3, a more explicative caption title would help the reader to quickly grasp the main conclusion from this figure.– It is not completely clear in how far the approach presented in this work can be extended to other antibiotics and their resistance targets. It would be interesting to discuss this in more detail in the manuscript.eLife. Your revised article has been evaluated by George Perry (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Development of antibacterial compounds that block evolutionary pathways to resistance" for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:The reviewers and I appreciate the numerous modifications that were made to the manuscript. You will find below some comments from the reviewers. I will not reproduce them here because I believe they are all important and would need to be addressed. Also, I appreciated the additional experiment to isolate resistant mutants (Line 464). However, all mutants isolated come from the same culture, so it is not possible to know whether they are independent mutants. This experiment suffers from the same limitation as the previous experiment. Mutants from independent cultures would need to be isolated for them to be truly independent. The claim that resistance to CD15-3 does not evolve through mutations in folA would need to be expressed as < resistance to CD15-3 is less likely to evolve through mutations in folA> rather than <does not evolve through mutations in folA>. Saying that it does not happen would require a comprehensive analysis of mutants. The first comment of reviewer #3 also relates to this point.Reviewer #2:The authors made important improvements to the manuscript, addressing many key issues. In particular, demonstration of lack of inhibition of the human DHFR by CD15-3 is encouraging. This point should be highlighted by including the maximal concentration tested in footnote (b) of Table 1. The overall strategy is now better contextualized and has been strengthened by the additional data included in this version of the manuscript.It is indeed interesting that (lines 712…) "the CD15-3 inhibitory activity extends beyond the DHFR variants that were initially selected as targets for structure-based design and that "unplanned" D27E and W30 mutants get inhibited with WT like efficacy." The design strategy included known single/double mutations at positions 21, 26 and 28, indicated in Table 1; in the case of CD15-3 only one point mutant Ki was assessed. The Ki of CD15-3 for the other mutants used in the design strategy (Table 1) must be included, otherwise, there is no direct validation of the strategy. This is made obvious by the unclear conclusions of the cellular inhibition results, where the authors go to great lengths to attempt to isolate the effect of the inhibitors on DHFR in the cells . Therefore, validation of the strategy on the direct targets used in the design, the mutant DHFRs, should be done.Reviewer #3:The authors made a substantial effort and addressed most critical points in the revision. It is also very helpful that they made the preprint of the related article available online. However, there are a few remaining points that still need to be addressed:– In their response, the authors speculate that, because of the second target of the compound, overexpression of folA would be a less optimal evolutionary solution. This is vague and still not clear because the data show that overexpression of folA is an easy way to increase resistance, irrespective of any other drug target. Moreover, in the second article they show that overexpressing the other target (folK) leads to even higher resistance to the drug. Thus, there is a similar issue: Why do solutions that overexpress this target (which should be easily accessible by promoter mutations or gene amplification) do not appear in the evolution experiments? I understand that it may not be possible to elucidate this in a reasonable time frame, but it should be clearly stated and discussed in detail in the manuscript (not just in the response to the reviewer comments). This point is important because it changes the main take-home message from "It is not possible to evolve resistance to the new compound" to "There are the same straightforward ways to evolve resistance to the new compound as for trimethoprim but (for unknown reasons) they are not followed in evolution experiments."– Another point that is addressed in the response to reviewers but still needs to be transparently stated and discussed in the revised manuscript is how the IC50 of the new compound compares to that of trimethoprim. The most helpful would be to show the dose-response curves for the new compound and for trimethoprim in the same plot with absolute concentrations on the x-axis. A similar plot that directly compares the effect of overexpressing folA on both dose-response curves would also help. Essential revisions:The reviewers raised many points that would need to be addressed for the manuscript to be considered further. I outline here the critical elements that are essential to address.1) The new compounds are presented as new antibiotics in the context of medical application but they are active against the human DHFR and thus lack selectivity, which is a major issue in antibiotic development. The reviewers agree that because of this, this work could not be published under the theme of inhibitor development. It could potentially be rewritten as a model system for the examination strategies to delay the onset of resistance.We thank the reviewers for pointing out this issue. Indeed any potential antibiotic lead has to exhibit selective activity against the bacterial protein and be inactive against its human orthologue. However the activity data we had earlier presented in our manuscript was of CD15, the parent compound for our lead CD15-3. The work essentially focuses on the potential of CD15-3 as an antibiotic lead. We assessed the CD15-3 activity against human DHFR and found that, unlike parent compound CD15, it does not inhibit human DHFR  and hence shows selectivity against only bacterial DHFR.E. coli DHFR (PDB 1RX3) and human DHFR (PDB 1KMS), we found there are significant sequence differences between them in the ligand-binding domain. Different pairs of amino acids between E. coli DHFR and human DHFR include Met20: Leu22, Asp27: Glu30, Leu28: Phe31, Trp30: Tyr33, Ile94: Val115 . From the binding of CD15 and CD15-3 against the two proteins, the loss of inhibition on human DHFR for CD15-3 is probably due to the loss of the pi-pi interaction with Phe31. As for the E. coli DHFR, the corresponding amino acid is Leu28, and it does not affect binding CD15-3.CD15-3 structurally differs from CD15 with a naphthalene ring instead of a dimethoxy substituted benzene ring which can potentially explain loss of its with human DHFR. By comparison of the crystal structures of 2) The number of mutants sequenced is not large enough to rule out the possibility that resistance can emerge through mutations in folA. In addition, some of the mutants come from the same adapted populations, which means that they are not an independent sampling of resistance mutants. The claim that resistance mechanisms are different from that of Trimethoprim and do not involve folA itself is therefore not supported.50 compared to the naïve cells, much lesser than the enhancement observed in the “TMP-evolved” batch (8 to 200 fold compared to the WT/naive).We thank the reviewers for this critical comment. Indeed, in our last submission we presented limited evolution trajectories. In our current submission we have introduced two additional evolution trajectories. We carried out the same evolution experiments following the protocols earlier mentioned and allowed bacterial cells to evolve in presence of CD15-3 for over a month. Upon sequencing the “CD15-3-evolved” batch we did not observe any mutation in the folA locus. Further these evolved batches again showed a little more than two fold increases in ICIn addition, we carried out an alternative strategy of “one-shot” selection of colonies that are capable of growth in presence of TMP or CD15-3. Again, colonies grown in presence of CD15-3 do not show variation in the folA locus (see new Table 3 and accompanying discussion) and show a lower increase in resistance when compared to TMP.3) Some of the information that would have been useful for the interpretation and evaluation of the current manuscript is to be published in an upcoming publication. It would be important for the reviewers to have access to this information. This would be possible for instance by providing a link to a preprint with this other paper.We thank the reviewers for this point. We provided a link to the recently submitted Biorxiv pre-print of the paper discussing CD15-3 intra-cellular mode of action in the relevant parts of the manuscript.4) The concept of evolution drug needs to be better defined to make sure the reader understands its scope and what it is bringing to the field.We appreciate this suggestion. Earlier we had provided a brief overview behind the concept of “evolution-drug” in the introduction section. We had re-formatted that section and introduced some additional information also in the discussion to provide a better conceptual background. Corresponding changes in the Introduction and Discussion are marked in blue.We also transmit the detailed comments below.Reviewer #1:I cannot comment on the sections of the manuscript related to compound search and identification.My general comment is about what makes an evolution drug exactly. From the definition given, it is a drug that works on mutants that are resistant to other drugs. Most antibiotics that interact slightly differently with the same target, or that have other modes of action altogether would therefore be evolution drugs? Since this is the aspect of the paper that makes it very original, I would need to better understand what this concept is, what it is not, and what novelty it brings.We thank the reviewer for this point. In our earlier submission we had briefly mentioned what “evolution-drugs” mean and we have re-formatted that section. Also we have clarified the concept in the direction which reviewer had mentioned.Conceptually “evolution drug” is a compound which stresses pathogens or cancer cells and suppresses their evolutionary pathways to escape. Antibiotic resistance or drug resistance in general is an evolutionary selection of phenotype which evades the drug action by deploying multiple molecular level escape strategies. These strategies could be as general as efficient efflux of the drug or specific like mutation of the target locus resulting in the target protein modification which would eliminate binding of the antibiotic molecule. In our study we designed a strategy to have antibiotic leads which would not only interact with the WT protein but would interact with a subset of target protein escape variants. Since compounds designed like this can potentially block the evolutionary escape routes of bacterial cells growing under antibiotic selection, the strategy could be potentially promising to block the emergence of “antibiotic-resistant” bacterial phenotypes.My second critical comment regards the evolution experiment. I could not find exactly how many independent lines were evolved to resist the novel compound and how many were sequenced. The methods mention evolution in 96 well plates but from the results, it seems that only two mutants were sequenced. I believe that this is not enough to claim that this drug will not select for mutations in fola. To be able to show this, enough independent clones have to be sampled and sequenced to show that the space of resistance mutations has been completely sampled.We thank the reviewer for pointing out this issue. In our previous submission we had presented one evolution trajectory/line. Two colonies obtained after plating the evolved batch were selected and sequenced. In our current submission we have added two more evolution trajectories and we did not observe any mutation in the folA locus. Also we have carried out a parallel plate based study (new Table 3 and accompanying discussion) and found the colonies formed on CD15-3 plate do not have mutation in the folA locus compared to the ones on TMP plate which had mutations in the folA locus.My third critical comment is that it seems that CD15-3 selects for mechanisms that lead to resistance to TMP as well, which is not something that you want for an evolution drug. This means that resistance to CD15-3 brings resistance to TMP as well if I understand well. How does this fit within the concept of evolution drugs? This would seem to make it a rather inefficient one.50 compared to that of the naïve cells. But this resistance could be attributed to the transporters and pumps which constituted around 38% of the genes in the duplicated segment. This is a very generic mechanism by which evolving bacterial cells neutralize the toxicity of the drugs. We showed that a 30+ days of evolution under CD15-3 leads to the modest increase in IC50 but it does not involve any mutation in the folA locus. It is interesting to note that CD15-3 unlike TMP was not only designed to inhibit WT DHFR but also some prominent mutants of DHFR which emerge under TMP selection. Since CD15-3 significantly constraints the escape routes associated with folA locus mutation, it is likely that the resistance would emerge by generic mechanisms as mediated by efflux in the current variants.We thank the reviewer for this point and appreciate the observation. It is true that CD15-3 escape/evolved variant offered moderate resistance to TMP as was evident from 3 fold enhanced IC50 of the evolved variants were just 2 times compared to the naïve cells. During the same timescale of evolution, the IC50 of the TMP escape variants went to 8 to 200 times higher compared to that of the naïve cells.Further CD15-3 significantly delayed the onset of resistance as the ICDetailed comments: Panels of Figure 7 would need to be better labelled.Many barplots are used to show estimates, for instance in figure 4. This is a representation that poorly represents the data. . I recommend alternatives are used and all data points are shown in addition to error bars (which are not defined here).We thank the reviewer for pointing out these two points. We changed the Figure 7 in the current submission.The reviewer suggested us to change our bar graph in Figure 4 to an alternative one. However, when we generated the bar graph in Figure 4, we considered three replicate tests for the inhibition ratios and then took an average of the ratios and converted them into average ki value and its standard error.Reviewer #2:The authors present a computational workflow that convincingly demonstrates the design and optimization of inhibitors to a key antibiotic target, such that the development of inhibitor resistance is clearly delayed. The experimental determination of inhibition in vitro, in cells and during the course of one month under evolutionary pressure with the new compounds was convincing. Unfortunately, the new inhibitors inhibit human DHFR more strongly. They would be toxic/lethal.The authors could examine the inclusion of negative design (against inhibition of the human enzyme) in their workflow.We thank the reviewer for pointing out this concern. Table 1 in our earlier submission primarily documented the CD15 activity profile. It is to be noted that CD15-3 which we present as the potential antibiotic lead is a derivative of CD15. We carefully measured the activity of CD15-3 against human DHFR and found that CD15-3 does not show any inhibitory activity . Also in the current submission we have incorporated a table showing the in vitro activity profile of CD15-3.E. coli DHFR inhibitors, their activity against human DHFR were not considered in the computational search. We only used the E. coli DHFR crystal structure for the virtual screening. Later we tested CD15-3 against human DHFR and found that CD15-3 unlike CD15 did not inhibit human DHFR activity.Indeed in our earlier submission when we tried to find novel We thank the reviewer for the valuable suggestion on negative design. We would include a negative design in later steps of the screening workflow in our future efforts and calculate the binding activity of the hit compounds against human DHFR and remove those compounds with high affinity against human DHFR.E. coli WT and L28R DHFR and human DHFR by using molecular dynamics and MM/PBSA. We found that the binding free energy for TMP is lower on the human DHFR than on E. coli WT and L28R DHFR. This can partly explain why TMP is highly potent against microbial DHFRs than the human DHFR. As can be seen from E. coli WT or L28R DHFR.But in this work, since the hits were already screened, so we would like to try to use this method to further optimize the hits to improve their activity. Nevertheless, we calculated the binding free energy of TMP against a Two replicates of 10ns molecular dynamics simulation were conducted for each system, and the last 2ns trajectory were used to calculate the binding free energy of each simulation and obtain the average. bEcDHFR represents E. coli DHFR. chDHFR represents human DHFR.E. coli  making DHFR an appealing target to develop evolution antibiotic drugs. » This view is now known to be incomplete. Crucially, the folA-related DfrA enzyme family should be introduced  because clinical TMP resistance is more complex than point mutations to FolA as described by Huovinen. The intrinsically TMP-resistant DfrB family of DHFRs should also be mentioned as being part of the problem, although this work would not directly apply to it.Since trimethoprim resistance is at the center of this research, and that the authors aim to develop an "evolution drug", TMP-specific resistance should be described in greater depth. The specific context and reference given in the 2nd/3rd paragraphs of the introduction are: "However, due to rapid emergence of resistant mutations in DHFR, the development of drug resistance to antifolate antibiotics belonging to any of the above-mentioned classes presents a significant challenge . Both clinical and in vitro studies have shown that accumulation of point mutations in critical amino acids residues of the binding cavity represent an important mode of trimethoprim resistance. Mutations conferring resistance in bacteria to anti-DHFR compounds are primarily located in the folA locus that encodes DHFR in As a result of the new finding that TMP resistance is very diverse and does not result only from the recent evolution of point mutations as was previously thought, the approach presented here should be qualified. In the introduction (3rd paragraph): "In this work, we developed an integrative computational modeling and biological evaluation workflow to discover novel DHFR inhibitors that are active against WT and resistant variants. » should qualify the resistant variants as being specifically directed to the point-mutated variants only, not all resistant variants that are genetically diverse.It thus follows that the premise, as currently stated, is somewhat misleading. The introduction discusses clinical observation of antibiotic evolution. However, the point mutations under study are not particularly relevant to the natural evolution of TMP resistance (prior to introduction of TMP), or even its clinical evolution. This is clearly shown by the modest TMP resistance offered . As described under Methods, section "Construction of binding affinity prediction model": "An evolutionary study  of TMP resistance found that three key resistance mutations P21L, A26T, L28R, and their combinations constitute a set that recurrently occurred in two out of five independent evolution experiments, and their order of fixation in both cases was similar." This set of mutations constitutes a simple model system of in vitro evolution. This does not remove from the success of the inhibitor discovery and delay of onset of resistance, but it must be made clear that the premise is limited to in vitro evolution as a model of natural evolution, from the Abstract and throughout the body of the work.We thank the reviewer for this extensive suggestion on better putting the perspective of resistance against TMP. It is true that the TMP resistance under clinical setting is far more complex and the recent studies cited are extremely insightful in that direction. We have re-drafted the sections pointed and included the studies suggested. In particular we note in lines 94-95 “In our current study we focus on a simplified model system of in vitro evolution whereby resistant phenotypes emerge from point mutations in the folA locus.”E. coli FolA? Therefore, do they demonstrate broad predictive power, as stated, and does it suggest that they are broadly efficient potential antibacterial leads ?Following Figure 1: "…Listeria grayi (L. grayi) and Chlamydia muridarum (C. muridarum) again showing highly significant correlation between predicted and experimental values , demonstrating broad predictive power of the method". To highlight the relevance of the study, the choice of Listeria grayi and Chlamydia muridarum should be rationalized. The native resistance of L. grayi should be expressed; the choice of C. muridarum is not so clear. Importantly, what is their evolutionary distance with Listeria grayi (L. grayi) and Chlamydia muridarum (C. muridarum) for verifying the binding affinity prediction linear model is as follows.We thank the reviewer for the important point raised. The reason why we also include E. coli cells with plasmids overexpressing DHFR from two mesophilic bacteria viz. C. muridarum and L. grayi, sharing 26% and 36% sequence identity with E. coli DHFR, respectively and carrying all combinations of the three key mutations in the loci corresponding to the three escape mutations in E. coli DHFR that we study here .In one of the former study from the group  depicting broader view of the biophysical nature of the fitness landscape of antibiotic resistance we transformed E. coli DHFR we purified all variants of the mutant orthologs from C. Muridarum and L. grayi, characterized their biophysical properties, and compared them with E. coli DHFR mutants . We observed a similar trade-off between catalytic activity and TMP binding . ” .“Likewise in this work apart from E. coli, C. Muridarum and L. grayi respectively, which enabled us to construct the binding affinity prediction linear models. And our results  show that highly significant correlations between predicted and experimental values are obtained for all three species, demonstrating broad predictive power of the method.Using DHFRs from two additional sources allowed us to significantly extend the dynamic range of biophysical parameters to provide a comprehensive biophysical mapping of the fitness landscape. Also, we obtained the inhibition data for TMP against WT and three point mutant DHFR from The authors chose to characterize the in vivo IC50 of the compounds. Since the role of compounds is to inhibit bacterial growth completely, minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) might be more relevant in this context and could be determined for the most interesting compounds, and different targets.50) to the zero antibiotic concentration is reached. Since the compounds had limited solubility at high concentrations, going beyond a certain compound concentration to derive minimal inhibitory or batericidal concentration was not possible.For testing the antibacterial property of the compounds described we measured the effective concentrations of the compounds at which half of the growth rate compared This article has been cited in the introduction.– Confusion in describing the activity test in vitro: in the body the authors describe fluorescence at 340 nm  whereas Methods describe the decrease in absorbance at 340 nm. Figure 2A: Which is the curve of TMP?We used both absorption and fluorescence at 450 nm (excitation 340 nm) to assay the inhibition of DHFR catalytic activity. The initial screen of the 40 compounds was made by measuring the rate of NADPH oxidation monitored the decrease in absorbance at 340 nm. For the concentration dependence studies we measured NADPH oxidation by monitoring the fluorescence at 450 nm (excitation at 340nm), to avoid interferences at high compound concentrations. We clarified this aspect in the revised version. Figure 2A represents an example of in vitro kinetic assay of DHFR catalytic activity. This figure illustrates the inhibition of DHFR at by the hit compound CD15 at different concentrations. The bottom two lines are controls without CD15 but using equal amount of DMSO instead.– Table 1: significant digits on values are not uniform – should be two.The significant digits on values from Table 1 have been set to two.E. coli and in vitro are not routinely italicized,– E. coli and in vitro are all italicized in the whole manuscript.– CD15-3 or CD15.3.We keep CD15-3 for the compound ID. Typographic errors of CD15.3 were corrected.– Table 1: Ki of TMP should be included for comparative purposes.The Ki values for TMP were added to Table 1.– Table 2: IC50 values should have no more than 2 significant digits. TMP should have STD.This was corrected.– Figure 7C: Concentration in µM, not µm.This was corrected.E. coli variants presented in panel E. In each DIC image, there should be a size bar (ex. 5 µm).– Figure 9. The order of the panels should be the same as the order of the This was corrected.Reviewer #3:In this manuscript, the authors present a strategy to identify new antibiotic compounds that block DHFR -- the target of trimethoprim -- in a way that should still work in DHFR variants carrying common trimethoprim resistance mutations. Their strategy consists of a computational approach to select promising candidates from known databases, followed by an experimental optimization and validation of the identified compounds. Interestingly, the first computational steps already narrowed down large chemical databases into a few relevant compounds, which are then shown to be efficient DHFR inhibitors in the experimental tests. Moreover, evolution of resistance to the resulting compound is shown to be much slower than resistance to trimethoprim.The authors should address the following points (roughly in order of importance):– An upcoming publication, in which a second molecular target of compound CD15-3 is revealed, is mentioned several times. It is slightly frustrating that these results, which may be key to understanding some of the phenomena reported in the present work (see below), are not included in this work. Why can this information not be included in the current article?We thank the reviewer for the point mentioned. In our current submission we have provided the link to our pre-print “” at relevant places in the manuscript.– Apart from non-synonymous mutations in folA, a common alternative way to evolve trimethoprim resistance is to overexpress DHFR. This happens in typical evolution experiments and in the clinic, for example through mutations that affect the folA promoter ; this could also happen via duplication (or further amplification) of the folA gene. Figure 6 shows that, indeed, for the CD15-3 compound, overexpressing DHFR would actually allow the cell to recover from the effect of the compound. It is thus quite striking that no overexpression of DHFR seems to occur in the evolution experiments shown in figure 7A, where no mutation in folA fixes. The authors should comment on this in more detail. Could the other (mysterious) target of the drug explain this?We thank the reviewer for this observation. It is true that a common way to evolve resistant against anti-folates like TMP is to overexpress DHFR. We did not observe any overexpression DHFR due to upstream mutation and it could be because that CD15-3 has an additional non-DHFR intra-cellular target . This makes resistance by gene overexpression a less evolutionarily optimal solution. Resistance by overexpressing the pumps constituting the gene-duplicated segment provides a much more generic solution leading to CD15-3 resistance.– How does the absolute value of the IC50 of the new compound compare with that of trimethoprim?It is about 100 fold higher but the relevant comparison is with escape variants such as L28R where it is 3 fold lower than TMP.– The manuscript has a considerable amount of technical details in the main text, which makes it unnecessarily long and sometimes difficult to follow the main ideas. A shorter main text with fewer figures would considerably increase the clarity and accessibility of this work for non-experts (the details could be shifted to a supplement or appendix).eLife does not allow supplementary text per their style. Therefore, we had to incorporate many important details in main text but tried to do our best to make the paper modular so that a reader not interested in this or that detail of the multitool approach can omit them without sacrificing general understanding of the flow of logic and results.– In figure 3, a more explicative caption title would help the reader to quickly grasp the main conclusion from this figure.The caption title of Figure was revised to “Figure 3- The inhibition effect of compounds CD15 and CD17 against WT and mutant DHFR from different species.”– It is not completely clear in how far the approach presented in this work can be extended to other antibiotics and their resistance targets. It would be interesting to discuss this in more detail in the manuscript.We thank the reviewer this point. We have incorporated some of the relevant details in the current submission.E. coli DHFR as the drug target and used structure- and ligand-based drug design methods to screen compound databases in an attempt to find hits that can not only inhibit WT DHFR but would inhibit TMP-escape mutant forms of DHFR. In the computational workflow, a variety of computational methods were carried out for virtual screening, such as molecular docking to simulate drug target interactions and screen large compound libraries, molecular dynamics combined with free energy calculations to construct drug target affinity prediction models, We obtained potent inhibitors that were inhibitory to WT and three TMP escape mutants. We found that CD15-3 was also effective in inhibiting some other TMP escape mutants which emerged from our in-vitro evolution experiment.In the present work we considered When this method is applied to other targets with an aim to address the problem of drug resistance, we can consider building a predictive model from known inhibitors against resistant mutations, which may contribute to the drug discovery process. Some of the related references are shown as follows.1. Perryman A L , Lin J H , Mccammon J A. HIV-1 protease molecular dynamics of a wild-type and of the V82F/I84V mutant: Possible contributions to drug resistance and a potential new target site for drugs[J]. Protein Science, 2004.2. Zhang Y , Shen H , Zhang M , et al. Exploring the Proton Conductance and Drug Resistance of BM2 Channel through Molecular Dynamics Simulations and Free Energy Calculations at Different pH Conditions[J]. Journal of Physical Chemistry B, 2013, 117(4):982-8.3. Hao, Zhang, Xinheng, et al. How does the novel T315L mutation of breakpoint cluster region-abelson (BCR-ABL) kinase confer resistance to ponatinib: a comparative molecular dynamics simulation study [J]. Journal of biomolecular structure and dynamics, 2019:1-12.4. Avilaq B A , Baday S. Investigation of Drug Resistance Mechanisms for Antiandrogen Prostate Cancer Drug Enzalutamide using Molecular Dynamics Simulations[J]. Biophysical Journal, 2020, 118(3):194a.5. Djoumbou F Y. Molecular Dynamics Based Prediction of HIV-1 Drug Resistance. 2008.6. Jian J T , Sun T G , Wei Z C , et al. Molecular Dynamics Simulation of HIV1 gp41 and the N554D/S649A Double Mutation for Drug Resistance to Enfuvirtide. IEEE, 2009.7. Zhou X , Du J , Zhou X , et al. Computer-aided design of PVR mutants with enhanced binding affinity to TIGIT[J]. Cell Communication and Signaling, 2021, 19(1).8. Sun L , Zhou Y X , Wang X D , et al. Ab initio molecular dynamics and materials design for embedded phase-change memory[J]. npj Computational Materials, 2021.Reference:Chowdhury, S., Zielinski, D.C., Dalldorf, C., Rodrigues, J.V., Palsson, B., and Shakhnovich, E. (2021). A systems-guided approach to discover the intracellular target of a novel evolution-drug lead. bioRxiv, 2021.2005.2017.444532.113, E1470-E1478.Rodrigues, J.V., Bershtein, S., Li, A., Lozovsky, E.R., Hartl, D.L., and Shakhnovich, E.I. (2016). Biophysical principles predict fitness landscapes of drug resistance. Proceedings of the National Academy of Sciences The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:The reviewers and I appreciate the numerous modifications that were made to the manuscript. You will find below some comments from the reviewers. I will not reproduce them here because I believe they are all important and would need to be addressed. Also, I appreciated the additional experiment to isolate resistant mutants (Line 464). However, all mutants isolated come from the same culture, so it is not possible to know whether they are independent mutants. This experiment suffers from the same limitation as the previous experiment. Mutants from independent cultures would need to be isolated for them to be truly independent. The claim that resistance to CD15-3 does not evolve through mutations in folA would need to be expressed as < resistance to CD15-3 is less likely to evolve through mutations in folA> rather than <does not evolve through mutations in folA>. Saying that it does not happen would require a comprehensive analysis of mutants. The first comment of reviewer #3 also relates to this point.Thank you for pointing this out. Now we included the results from two more independent starter-cultures. As before, we did not observe any mutation in the folA locus under the conditions of “one-shot” CD15-3 selection.Further, we changed/rephrased sections expressing “does not evolve through mutations in folA” to “resistance to CD15-3 is less likely to evolve through mutations in folA” (lines 712 and 719 to 723) as correctly pointed out by the editor.Reviewer #2:The authors made important improvements to the manuscript, addressing many key issues. In particular, demonstration of lack of inhibition of the human DHFR by CD15-3 is encouraging. This point should be highlighted by including the maximal concentration tested in footnote (b) of Table 1. The overall strategy is now better contextualized and has been strengthened by the additional data included in this version of the manuscript.It is indeed interesting that (lines 712…) "the CD15-3 inhibitory activity extends beyond the DHFR variants that were initially selected as targets for structure-based design and that "unplanned" D27E and W30 mutants get inhibited with WT like efficacy." The design strategy included known single/double mutations at positions 21, 26 and 28, indicated in Table 1; in the case of CD15-3 only one point mutant Ki was assessed. The Ki of CD15-3 for the other mutants used in the design strategy (Table 1) must be included, otherwise, there is no direct validation of the strategy. This is made obvious by the unclear conclusions of the cellular inhibition results, where the authors go to great lengths to attempt to isolate the effect of the inhibitors on DHFR in the cells . Therefore, validation of the strategy on the direct targets used in the design, the mutant DHFRs, should be done.We thank the reviewer for pointing out this oversight. In our current submission we added the Ki of CD15-3 for two additional mutants’ viz. P21L and A26T, (Table 1) which were included in our original CD15-3 design scheme.Reviewer #3:The authors made a substantial effort and addressed most critical points in the revision. It is also very helpful that they made the preprint of the related article available online. However, there are a few remaining points that still need to be addressed:– In their response, the authors speculate that, because of the second target of the compound, overexpression of folA would be a less optimal evolutionary solution. This is vague and still not clear because the data show that overexpression of folA is an easy way to increase resistance, irrespective of any other drug target. Moreover, in the second article they show that overexpressing the other target (folK) leads to even higher resistance to the drug. Thus, there is a similar issue: Why do solutions that overexpress this target (which should be easily accessible by promoter mutations or gene amplification) do not appear in the evolution experiments? I understand that it may not be possible to elucidate this in a reasonable time frame, but it should be clearly stated and discussed in detail in the manuscript (not just in the response to the reviewer comments). This point is important because it changes the main take-home message from "It is not possible to evolve resistance to the new compound" to "There are the same straightforward ways to evolve resistance to the new compound as for trimethoprim but (for unknown reasons) they are not followed in evolution experiments."We thank the reviewer for his critical comments. We have rephrased the relevant sections in the manuscript as per the suggestion. .– Another point that is addressed in the response to reviewers but still needs to be transparently stated and discussed in the revised manuscript is how the IC50 of the new compound compares to that of trimethoprim. The most helpful would be to show the dose-response curves for the new compound and for trimethoprim in the same plot with absolute concentrations on the x-axis. A similar plot that directly compares the effect of overexpressing folA on both dose-response curves would also help.50 values for TMP and CD15-3. Since TMP shows efficacy at a nano-molar level compared to micro-molar levels of CD15-3, a cumulative dose response plot is otherwise hard to plot.We thank the reviewer for this point. In figure 4E we have provided a comparative bar-plot of IC