The pace of drug discovery and approvals has led to expanding treatments for cancer patients. Although extensive research exists regarding barriers to enrollment in oncology clinical trials, there are limited studies evaluating processes to optimize patient education, oral anticancer therapy administration, and adherence for patients enrolled in clinical trials. In this study, we assess the feasibility of a video-based, personalized webpage for patients enrolled in genitourinary oncology clinical trials involving 1 or more oral anticancer therapy.The primary objective of this trial was to assess the differences in the number of patient-initiated violations in the intervention arm compared with a control arm over 4 treatment cycles. Secondary objectives included patient satisfaction, frequently asked questions by patients on the intervention arm, patient-initiated calls to study team members, and patient-reported stress levels.Eligible patients enrolling on a therapeutic clinical trial for a genitourinary malignancy were randomized 2:1 to the intervention arm or control arm. Patients randomized to the intervention arm received access to a video-based, personalized webpage, which included videos of patients’ own clinic encounters with their providers, instructional videos on medication administration and side effects, and electronic versions of educational documents.P value=.73). Median baseline satisfaction scores for the intervention and control arms were 72 and 73, respectively, indicating high levels of patient satisfaction in both arms. Median baseline patient-reported stress levels were 10 and 13 for the intervention and control arms, respectively, indicating low stress levels in both arms at baseline.A total of 99 patients were enrolled . In total, 71% (40/56) of patients in the intervention arm had 1 or more patient-initiated violation compared with 70% (23/33) in the control arm. There was no difference in the total number of violations across 4 cycles between the 2 arms (estimate=−0.0939, 95% CI−0.6295 to 0.4418, This study is among the first to evaluate a video-based, personalized webpage that provides patients with educational videos and video recordings of clinical trial appointments. Despite not meeting the primary endpoint of reduced patient-initiated violations, this study demonstrates the feasibility of a video-based, personalized webpage in clinical trials. Future research assessing this tool might be better suited for realms outside of clinical trials and might consider the use of an endpoint that assesses patient-reported outcomes directly. A major limitation of this study was the lack of prior data for estimating the null hypothesis in this population. Over the past several years, the Food and Drug Administration (FDA) has approved several new therapies for the treatment of genitourinary malignancies , and thiGiven that approximately 25% to 30% of the oncology drug pipeline involves oral anticancer therapy , researcAdherence interventions for patients in clinical trials have typically focused on providing oral anticancer therapy education at the time of clinical trial informed consent or initial investigational drug prescription. Experience demonstrates that trials involving oral anticancer therapy necessitate additional support for patients to optimally manage treatment outside the secure and supportive clinic environment .Before the rise of oral chemotherapy, patients typically received their treatments in the clinic where intravenous drugs could be administered in a safe, protected environment. Now, patients can take their oral chemotherapy at home without direct observation. The level to which adherence is an issue in oncology clinical trials is not clearly defined, and the degree of additional support patients require to properly self-administer oral anticancer therapy has not been quantified. It is known, however, that the strict requirements of a clinical trial  can cause patients to feel overwhelmed and confused. Research has shown that patients can have misconceptions about aspects of research, such as the risk of side effects, trial aims, and the likelihood of personal benefit .Research looking at the use of Web-based interventions and video content to improve oral medication adherence remains largely untapped, but data do suggest that Web-based interventions can effectively increase medication adherence among chronically ill patients, patients undergoing smoking cessation, patients initiating HIV treatment, and in other clinical scenarios -17. ResePhase 3 studies have shown that video content that is tailored to the cancer patient compared with standardized text can effectively improve patient knowledge related to clinical trials and reduce attitudinal barriers . FurtherIn this randomized phase 2 study, we assessed the use of a video-based, personalized webpage via the information-sharing platform, Postwire, to help patients navigate their participation in genitourinary oncology clinical trials involving 1 or more oral anticancer therapy. We used a novel and objective endpoint, patient-initiated protocol violations, for this study to quantitatively evaluate whether a personalized, video-based webpage might help clinical trial patients better adhere to the strict clinical trial guidelines. Patient-initiated protocol violations can be objectively measured and reflect patient misunderstanding regarding medication administration and other protocol procedures. We hypothesized that patients receiving the video-based intervention would have less patient-initiated protocol violations given improved understanding about mediation administration and trial procedures, which would be provided through the video-based, personalized webpage compared with a control arm. In addition, we hypothesized improved patient satisfaction and reduced stress.This was a randomized, phase 2 clinical trial assessing a video-based, personalized webpage as a complement to standard patient education for clinical trial patients. The institutional review board (IRB) at the Dana-Farber Cancer Institute (DFCI) approved this intervention and determined that this randomized, phase 2 trial did not meet the requirements for registration to a WHO-accredited trial registry as it was an ancillary, noninvasive trial.The Postwire application was chosen as it is Health Insurance Portability and Accountability Act (HIPAA)–compliant and uses secure sockets layer technology to prevent hacking and ensure privacy. All patients provided written informed consent. This study was pilot-tested before the initiation of patient recruitment and enrollment to optimize workflow and minimize technology malfunctions. A safety run-in of 9 weeks took place to carefully monitor patients and protocol procedures, and adjustments were made as necessary to optimize workflow and study conduct.parent clinical trial protocol type . Patients randomized to the control arm received standard clinical trial oral anticancer therapy education and care without a video-based personalized webpage. Patients enrolled in the intervention arm had access to a video-based personalized webpage in addition to standard of care educational materials.The study consisted of 2 arms: the video-based intervention arm and the control arm. Patients were randomized 2:1 to the intervention arm or control arm and were stratified by therapeutic or patient designees).Patients assigned to the intervention arm were introduced to the Postwire interface prior to cycle 1 day 1. A designated clinical research coordinator used a tablet to show the patient how to navigate the personalized webpage using a template page. The study team obtained the patient’s email address, and within 24 hours of the patient’s cycle 1 day 1 clinic visit, a secure email containing a link to the patient’s personalized webpage was sent. The next 3 clinic visits with the patient’s oncology provider were recorded in real time and uploaded to the patient’s personalized webpage. This webpage included videos of the patient’s own clinic encounters, their parent clinical trial informed consent, emergency contact information, oral anticancer therapy dosing guidelines, and medication diaries. When new or revised content was uploaded to a patient’s personalized webpage, a notification email was sent. In addition to the video recordings of patient’s encounters with their providers, the webpage was equipped with templated videos of the patients’ own nurses describing how to self-administer oral anticancer therapy , what siThe personalized webpage was available to patients on the intervention arm until 6 full cycles had been completed. Once data collection was complete, the webpage was deleted. If a patient withdrew from the parent, therapeutic clinical trial, or this ancillary trial, the webpage was deactivated. All patients on the intervention arm received paper-based instructional documents and were encouraged to contact the study team with any questions. This intervention was a complement to standard practice rather than a replacement. Patients randomized to the control arm received the same educational content in paper form and were educated by their treating oncologists and care teams according to standard practice.This ancillary study’s treatment cycles matched the patient’s parent clinical trial treatment cycles, which ranged from 21 to 42 days except for 1 trial which had 90-day treatment cycles after the first 28 days. Patients remained on the intervention for 6 cycles (4 cycles of the intervention and 2 cycles of follow-up), withdrawal of consent, or until meeting parent trial discontinuation criteria.Patients with genitourinary malignancies enrolling in a clinical trial involving 1 or more oral anticancer therapy were eligible if they had consented but not yet started on 1 of several selected parent clinical trials. When a patient was consenting to 1 of the selected parent clinical trials, the provider would discuss this ancillary trial and assess patient interest. Eligible patients were English-speaking and had adequate internet competency and use, as determined by a 3-point scale eligibility questionnaire. Patients’ level of functioning and ability to perform activities of daily living as well as physical ability were recorded using the Eastern Cooperative Oncology Group (ECOG) Scale of Performance Status and included in the baseline characteristics. Support was not available to develop videos in multiple languages. Patients who were interested and eligible were consented at the same time they were consented to the parent clinical trial. If the provider was unable to collect consent to this ancillary trial on the day of parent trial consent, it was possible to obtain consent and receive registration and randomization status before the initial cycle 1 day 1 clinic visit. The IRB deemed this trial low risk and determined it acceptable for clinical research coordinators trained on the study to consent patients, if delegated by the principal investigator.The primary objective of this trial was to assess the differences in the number of patient-initiated violations in the intervention arm compared with the control arm over 4 treatment cycles. Patient-initiated protocol violations reflect incidents of patient misunderstanding that could potentially be improved upon with the personalized, video-based intervention. Although patient-initiated protocol violations could be measured quantitatively and objectively, the degree to which patient-initiated protocol violations affect the clinical trials process and the rate at which they occur was not known at baseline. This was the rationale for choosing this novel endpoint. Secondary objectives included assessment of patient satisfaction as measured by the Functional Assessment of Chronic Illness Therapy-Treatment Satisfaction-Patient Satisfaction (FACIT-TS-PS) , patientPatient-initiated violations were defined as events that deviated from instructions detailed in the clinical protocol and including inappropriate drug dosing and operational or safety-related events. All instances of violations were recorded by cycle. To give equal weight to the 11 violations, a given violation, no matter how often it occurred within a cycle, counted once per cycle. For example, a patient would have 2 patient-initiated violations if he missed 2 doses (counted as 1 violation) and self-administered 3 doses at the wrong time (counted as 1 violation). We chose to calculate violations as such because each violation reflected 1 episode of patient misunderstanding. Consequently, a patient completing 4 cycles would have a count ranging from 0 to 44 patient-initiated violations.Postwire allowed for internal tracking of user access, and individual patient and patient designee access was recorded. A designated clinical research coordinator documented individual webpage accession on a weekly basis and inserted the data into the electronic data capture system.The FACIT-TS-PS and PSS-10 were collected at each day 1 visit for 6 cycles. The FACIT-TS-PS was used as it is an expansion of the Functional Assessment of Cancer Therapy scale, used specifically in the cancer population. FACIT questionnaires have been robustly validated and address many realms of psychology including physical well-being, social and family well-being, emotional well-being, and functional well-being . The FACThe PSS-10 predicts both objective biological markers of stress and increased risk for disease among persons with higher perceived stress levels. The PSS-10, which has been externally validated, was chosen because it is one of the most widely used assessments to measure perceived stress and allows patients to appraise what aspects of their lives are stressful . In addiQuestions asked during the filmed patient encounters were retrospectively reviewed and placed into predefined categories by a dedicated clinical research coordinator assigned to this trial. Questions were collected to assess patient clinical trial–related educational needs for future development of educational materials.Standard practice among the DFCI genitourinary clinical trial study team is to document any outgoing or incoming phone calls in the patient electronic medical record. In the event that incoming or outgoing calls were not documented, a clinical research coordinator contacted providers whose patients were participating on this ancillary study for additional phone data. This information was entered into the electronic data capture system unique to this study.To determine an appropriate sample size, the number of cumulative patient-initiated violations over 4 treatment cycles was simulated under a Poisson distribution, accounting for various proportions of patients without any patient-initiated violations. The intervention and control arms were compared with a 1-sided Wilcoxon rank-sum test 3000 times. On the basis of these simulations, the initial sample size was estimated at 75 patients, 25 patients in the control arm and 50 patients in the intervention arm, resulting in 93% power  to detect a 1:3 rate of patient-initiated violations in the intervention arm to the control arm equivalent to a rate ratio of 33%. This assumed 50% (12.5/25) of the control arm patients would have 1 or more patient-initiated violation or violations. Power was reduced to 80% if fewer patients experienced 1 or more patient-initiated violation or violations (33%).Given early dropout of patients from their parent clinical trials , the sample size was expanded from 75 to 99 patients (66 intervention: 33 control) to enable well-powered analyses based on the original hypothesis. Patient dropout was because of our patient population’s aggressive and advanced disease states. A total of 61 out of 89 (69%) patients were enrolled in parent trials related to metastatic cancers of the genitourinary system . Given pOn the basis of the statistical design, the primary analysis was to compare the intervention and control arms using the Wilcoxon rank-sum test . The primary endpoint was also evaluated as a violation rate , and the comparison between 2 arms was analyzed using a Poisson model. With the Poisson model, patients who dropped off before 4 cycles contributed to the endpoint for as long as they remained in the study. A log-linked model was applied for rate as a function of the predictor variable. By including the log of the treatment cycles into the model, treatment cycles became the denominator of log of the rate of total number of violations. Pearson chi-square was used to estimate the dispersion parameter to account for overdispersion in the model. Standard errors of regression coefficients were adjusted as well. As a sensitivity analysis, exact Wilcoxon rank-sum test adjusting for ties was used to assess whether total number of violations differed by arms in patients who completed all 4 cycles. Descriptive statistics were used to summarize secondary endpoints by arm and cycle.A total of 99 patients were enrolled from September 2014 to November 2016 . In totaextremely confident in their use of the internet, whereas the remaining patients  were somewhat confident in their use of the internet.Most patients had metastatic disease , were enrolled in parent trials of hormonal therapy , and had ECOG performance scores of 0 . The ECOG performance scale is routinely used to assess how patients’ cancers are progressing holistically. A score of 0 means a patient is fully active, and a score of 5 means death . In totaOf the 89 patients analyzed, 71% (40/56) of patients in the intervention arm had 1 or more patient-initiated violations compared with 70% (23/33) of patients in the control arm. For the intervention arm, the range of violations was 0 to 11, with a median of 1 in patients who completed greater than or equal to 4 cycles. For the control arm, the range of violations was 0 to 7, with a median of 1 in patients who completed greater than or equal to 4 cycles. For all patients who completed greater than or equal to 4 cycles, the range of violations was 0 to 11, with a median of 1. In total, 71% (63/89) had 1 or more violations over the course of 4 cycles.P value=.73). As a sensitivity analysis, the total number of patient-initiated violations was summarized for patients who completed 4 cycles of the intervention. There was no difference in total patient-initiated violations between arms (P=.92) in the 66 patients who completed greater than or equal to 4 cycles.Improper dosing , improper concomitant medication administration , and wrong doses  were among the patient-initiated violations. The most common patient-initiated violations were procedural in nature and included failing to return unused medication for pharmacy drug accountability , failing to return medication diaries , and incomplete medication diaries . There was no difference in the total number of patient-initiated violations across 4 cycles between arms  of patients assigned to the intervention arm (including 9 designees) accessed the video-based, personalized webpage and its subcontents during cycle 1, 45% (25/56) during cycle 2, 27% (15/56) during cycle 3, and 25% (14/56) during cycle 4. During the 2 follow-up cycles, 5 patients accessed the webpage during cycle 5, and 1 patient accessed the webpage during cycle 6.The median baseline satisfaction score for the intervention arm was 72 and 73 for the control arm . FollowiThe most frequently asked questions by patients during the video encounters were tabulated for individuals in the intervention arm. Most questions asked were related to drug administration, drug side effects, and scheduling .Overall, 21% (19/89) of patients in this study called an oncologist or nurse practitioner  at leastIn this study, we evaluate a video-based, personalized webpage as a complement to routine clinical trial patient education. Our trial did not prove that the video-based, personalized webpage intervention reduced patient-initiated violations. This result might in part be related to the low total number of patient-initiated violations in both arms. Nearly one-third of patients had no violations, and 27% (24/89) had only 1 violation over the course of 4 cycles. We suspect that given the sophistication needed to seek care at an urban tertiary cancer center and enroll in, not 1 but 2 clinical trials , our patDue to the exclusion criteria of the parent clinical trials and the lack of resources necessary to create materials for non-English-speaking patients, only English-speaking patients were included in this study. Research has touched upon the lack of minority representation in clinical trials. Video education has been shown to increase non-English-speaking patients’ understanding of consent information compared with oral education and home visits and has helped with recruiting and retaining non-English speaking patients in clinical trials . This viThe FACIT-TS-PS and PSS-10 results show that patients were satisfied with their research teams and stress levels were low in both arms. We suspect that this is because patients who remained on study were deriving clinical benefit from their parent clinical trials, thus impacting their satisfaction and stress levels. Though generally patients were satisfied with their care teams, experienced low levels of stress, and were relatively compliant regarding patient-initiated violations, there remain opportunities for continued improvement of communication and education in the clinical trials process ,19.On the basis of patient feedback in the form of emails to the study team, patient caregivers appeared to derive benefit from the intervention. Future work can assess the effect of similar interventions on patient family members or caregivers as a means of supporting the patient and improving the clinical trial experience. Future research might also consider enabling webpage communication functions, allowing researchers to assess the utility of real-time, Web-based modes of communication over standard methods . Video rTo objectively quantify the impact of the video-based, personalized webpage, we selected an innovative primary endpoint for this study: patient-initiated violations. Developing the primary endpoint was challenging given the lack of clinical studies of this type. We chose patient-initiated violations because this endpoint was quantifiable and clinically meaningful. Furthermore, we thought that this novel primary endpoint would serve as a surrogate of patient knowledge of trial medication adherence, administration, and overall clinical trial procedures. Defining the null and alternative hypotheses at the onset of the study was also difficult, as there are no published examples of patient-initiated violations in similar patients. An alternative endpoint that has been used in other studies of video-based education delivery includes patient-reported outcomes of knowledge, attitude, and preparation for making decisions about clinical trials . Such anThe intervention might have broader impact in a less resourced setting or community practice to enhance clinical trial procedures and patient education as well as in settings outside the realm of clinical trials. This trial, which was conducted at a single institution with extensive experience in clinical trial operations, required patients to have access to the internet and some degree of confidence in internet use. These enrollment criteria might have biased the patient population to those with the most potential for understanding clinical trial procedures and excluded those who could have experienced the greatest benefit.Strategies to incorporate Web-based and mobile communication on clinical trials are underway, such as the Preparatory Education About Clinical Trials, a Web-based and interactive computer program delivering educational content to patients considering clinical trials, and a Web-based prostate cancer treatment decision aid assessing treatment satisfaction, decisional regret, and quality of life ,21,32. AAs the population in general becomes more skilled with technology, we believe this type of intervention will be useful for patients and providers of varying disciplines. Although the primary endpoint of reduced patient-initiated violations over 4 cycles of treatment in the intervention arm was not met, this study demonstrates the feasibility of providing patients and caregivers with instant, at-home access to the discussions that take place between patients and their care teams. There was no difference between arms at the end of 4 cycles or following 2 cycles of follow-up. Nevertheless, our video-based personalized webpage offered patients an alternative to impersonal, lengthy, and written information as is currently provided in informed consent documents. Our intervention allowed patients to store, share, and access complex discussions related to their cancer care repeatedly over extended periods. With the ever-increasing role of technology in health care, the potential of a video-based, personalized webpage in oncology care includes optimization of oral anticancer therapy adherence, management of drug side effects, increase in patient safety, and improved trial operational quality and efficacy in a sustainable and translatable way that could benefit patients and providers to come.


Curable disease tuberculosis is becoming incurable or difficult to treat due to drug resistance. Multi drug resistance tuberculosis is a major health problem for less developed countries. Development of drug resistance is mainly as result of man related factors and poor lifestyle. Identifying predictors of drug resistance and working on them is the important way of reducing the expansion in high burden countries. Ethiopia is one of TB, TB/HIV, and multi-drug resistant tuberculosis (MDR-TB) high burden country globally. This study was aimed to assess predictor of MDR-TB in southwest part of Ethiopia.Unmatched case control study was conducted in case to control ratio of 1:1.2 in southwest part of Ethiopia. The cases were recruited from confirmed MDR-TB patient enrolled on second line treatment in Shenen Gibe Hospital (MDR-TB treatment center of the prefecture) and the controls were recruited from previously TB patients who cured or patient with smear negative at the end of treatment month during the study period in the same area. The data was collected by structured questionnaire by interview and logistic regression analyses were used to identify predictors of MDR-TB. Odds ratios with 95% CI were computed to determine the predictors.From the total 132 participants about 45% of them were cases. None disclosed tuberculosis infected to relatives , insufficient instruction on how to take anti-TB drug , contact history with MDR-TB , interruption of first-line anti-TB treatment for at list 1 day , and having alcohol drinking habits  were identified predictors for MDR-TB infection in study area.TB infection disclosure status, insufficient instruction on drug usage, contact history with MDR-TB, interruption of first-line anti-TB drugs, and alcohol drinking habits were identified predictor of MDR-TB case. Therefore, early detection and proper treatment of drug susceptible TB, strengthening directly observed treatment, short-course on daily bases, community involvement, and supporting the patient to intervene identified factors is paramount. Tuberculosis resistant to Isoniazid and Rifampicin, with or without resistance to any other drugs are became a major health problem all over the world. Multi-drug resistant tuberculosis is mainly manmade crisis related to multiple factors such as poor drug supply management, inappropriate guideline, none adherence, meager infection control practice, improper drug storage conditions, wrong dose or combination, and lack of information no monitoring of treatment , 2. AccoMulti drug resistant tuberculosis cases can be in the acquired or transmitted. Acquired drug resistant TB is the development of drug resistance in patients undergoing treatment with first-line drug while infection with resistant bacteria (transmitted) primarily can be occurred. Resistance of microorganisms is caused by a genetic mutation that makes a drug ineffective. Poor administration or insufficient treatment regimen allows drug resistant mutants to become the dominant strain in infected patient. Studies showed multiple factors that associated with development of the drug resistance in different geographical locations , 4–6. IdChallenges include fear of stigmatization which can discourage patients from presenting for treatment and follow-up care and taking medication in front of others; poor drug adherence and medication-associated side effects were common and contributing to high prevalence of MDR-TB. Any delay in diagnosing MDR-TB also impairs a patient’s prognosis and increase the risks of transmitting the disease within the community. MDR-TB is associated with a two to four fold period of treatment, psychological problems, economic wastage, poor treatment adherence and consequently treatment failure. It is also associated with higher case fatality rates .In the face of the high burden of TB in sub-Saharan Africa, the prevalence of MDR-TB increased due to HIV infection. Tuberculosis is still the major public health problem with high mortality and morbidity in developing counties. According to 2017 WHO report, Ethiopia is among the high burden countries with regards to TB, TB/HIV, and MDR-TB . HoweverAn unmatched case–control study was carried out from August 2016 to April 2017. The study was conducted in Jimma zone, Oromia regional state of Ethiopia at Shenen Gibe hospital. According to the Ethiopian 2007 population and housing survey the projected total population of the zone was 3,214,716 . The zonThe cases were recruited from confirmed MDR-TB patient enrolled on second line treatment in Shenen Gibe Hospital (MDR-TB treatment center) during the study period. The controls were recruited from previously TB patient but declared cure, patients who had completed treatment, and patient with smear negative at the end of treatment month during the study period. MDR-TB patient not on treatment during the study period and seriously ill patients were excluded.Epi-Info version 7.2.1 was used to calculate the number of study participant included in the study. Previous study conducted in the Amhara regional state  was usedMDR-TB patients enrolled to second line drug at Shenen Gibe Hospital from 2015 to 2016 were taken as cases and simple random sampling technique was used to include study subjects from the list of MDR-TB patient TB register. While Controls were also selected from randomly selected health facility in the zone . After selecting health facility, TB register was adjusted for controls meet inclusion criteria and sampling frame was made. Finally controls were randomly selected by simple random sampling technique of lottery method from selected health facility.A standardized investigators administered questionnaires were prepared in English and converted to local language (Afan Oromo) language by fluent speaker to collect information from the participant and converted back to English to check the reliability of data. Health officers and clinical nurses were recruited for data collection. Secondary data was reviewed from TB and MDR-TB registration books. Patient charts and the data collection format were used to determine and record the patients’ initial TB episode. The main variables included in the study instrument were sex, age, occupation, educational status, marital status, monthly income of the family, patient residence, family size, estimated distance of residence from the health facility, smoking, alcoholism, knowledge regarding MDR-TB, means of transportation to reach the health facility, previous TB treatment, previous known contact with an MDR-TB patient, opportunistic infection, defaulting from treatment, total time of first treatment, adverse effects of anti-TB medication, and prior episodes of anti-TB treatment. Data quality control was checked by reviewing with standard national guidelines of MDR-TB and pre tested in the 5% of similar population who not included in the study.Collected data were entered to Epi-Info version 7.2.1 and exported to SPSS version 20.0 for further analysis. Data completeness and consistency was checked by running frequencies of each variable. Analyses of variables were made using descriptive statistics and logistic regression analysis to look into the predictor of MDR-TB. Bivariate followed by multivariable logistic regression analysis was used to identify predictor of MDR-TB infection. Adjusted odds ratios (AOR) and their 95% CI were used to look into the association between the dependent and independent variables.Ethical clearance was obtained from the institutional review board of Saint Paul’s Hospital Mellinium Medical Collage; clearance was also obtained from Oromia regional health bureaus and from each selected health facility. During the study, written informed consents were obtained from all of the participants included in the study. The right of participants to participate voluntary, with draw or jump any questions not wanted to answer were well respected. Confidentiality was maintained at all levels of the study. Oral assent and written informed consent were obtained from each parent of the children less than 16 years old. Patient involvement in the study was voluntary and those who were unwilling and wanted to quit their participation at any stage were informed to do so without any restriction.Drinking habit: an individual who has habit drinking alcohol in regular bases.Substance intake habits: patterned use of a substance which have stimulant effect.Treatment insufficient instruction: anti-TB drug treatment instruction which lacks important information such as how to take the drug, when to the take, and why to take the drug.Supervision by health workers: regular check up done by health care works on anti-TB drug treatment.The following are operational definitions of variables used in the studyA complete response rate of participant was 98% that results in a total of 132 participants included in the study. The case control study was conducted in case to control ratio of 1:1.2 to determine predictor of MDR-TB in southwest Ethiopia. Male participants account 71 (53.8%) out of all study participants with almost equal allocation for case and control. The age range of the participants was from 10 to 70 years while the median age of the respondents was 27.5 years with the mean and standard deviation of 30.2 ± 12.7 with no significant difference between case and control. The majority of participants were in age group from 25 to 34 followed by 15–24 years. About 58% of cases and 40% of control were urban dweller results about half of the overall study participant the study participant. One-third of study participant has no regular income and including this group about 60% of the study participant has less than 500 Ethiopian currencies. Two-fifth of the participants were illiteracy with equal distribution between case and control while the rest had different level formal education as described in the Table Medical characteristics of the study participants were assessed and presented in Table With major proportion of cases, about 34% of all the respondents were previously treated with first-line anti-TB treatment before current treatment. Two-third of the MDR-TB cases were previously treated with anti-TB drug. Of all the MDR-TB cases, 37 and 27% of them had history of treatment failures and relapse respectively. One-third of participant experienced recognizable side effect among all participant and this proportion raised to two-third in MDR-TB cases. According to MDR-TB patient response, regular follow up by health workers was observed in two-third of them and only 57% of the cases were well instructed about anti-TB drugs ]. Patient who got insufficient instruction about anti-TB drugs had nearly five times more chance of developing MDR-TB than those who got sufficient instruction . Patient who had contact history with MDR-TB were 8.5 times more likely infected with MDR-TB than their counterparts . Patients who interrupt previous first-line anti-TB treatment for at list 1 day had eight times more odds of developing MDR-TB . Having alcohol drinking habits had five times more change of getting MDR-TB in this study  (Table The medical condition and socio-demographic factors were identified during the study and a number of factors were independently identified as predictor of infection or development of multi drug resistant tuberculosis. In our study, about two-third of the MDR-TB cases were previously treated with first-line drug. This indicates that the large proportion of drug resistance may be developed in patient during the first treatment rather than transmitted. In Ethiopia different studies indicated that previous exposure to TB treatment was found to be the most significant risk factors of MDR-TB –16. ThesIn our study different factors became associated with MDR-TB infection. Factors such as absence of discloser infection by patients, insufficient instruction anti-TB drug users, contact history with MDR-TB, missed anti-TB drugs at least for a day, and drinking habit were independently identified as predictor of the infection. In current study absence of disclosure of the previous infection by patient had three times more chance of development of MDR-TB than those who disclose their infection status. Similar finding was observed in studies conducted in Addis Ababa, Ethiopia , and NepProper use of drug according to instruction is crucial to prevent resistance developed from miss use of drug. In our study, patient who got insufficient instruction on how to take anti-TB drugs had about five times more chance of developing MDR-TB than their counterpart. Study conducted in Addis Ababa showed that treatment not directly observed by a health worker was associated with development of multi drug resistant tuberculosis . InsuffiHaving contact history with MDR-TB or other chronic coughers was another predictor of MDR-TB in our study. Similar findings were observed in study conducted in Oromia region of Ethiopia , Amhara Drug interruption is important factors for development of drug resistant pathogenic strains. In current study, missed first-line anti-TB drug at list for 1 day was strongly identified predictor of MDR-TB cases. Study conducted in Addis Ababa Ethiopia also showed similar finding that those TB patient interrupt first-line anti-TB drug at list for a day was about thirty more chance of MDR-TB development . A systeSo far different study exhibited the role of behavioral factors in development of multidrug resistant tuberculosis. In our study, having history of drinking alcohol was about five times more risk for development of MDR-TB. Study conducted in Nepal , and ThaMajority of patients who develop multi drug resistant tuberculosis were on first-line anti-TB treatment. Patients who were not disclosed their infection status, patient who receive insufficient instruction on how take anti-TB drugs, patient who had contact history with MDR-TB, interruption of first-line anti-TB drugs for at list 1 day, and patient who had alcohol drinking habits were independently identified as predictor of MDR-TB cases. Using directly observed treatment, short-course effectively and working on identified factors is mandatory to reduce the development drug resistance.


A core set of intervention and treatment options are recommended by the World Health Organization for use against falciparum malaria. These are treatment, long-lasting insecticide-treated bed nets, indoor residual spraying, and chemoprevention options. Both domestic and foreign aid funding for these tools is limited. When faced with budget restrictions, the introduction and scale-up of intervention and treatment options must be prioritized.Estimates of the cost and impact of different interventions were combined with a mathematical model of malaria transmission to estimate the most cost-effective prioritization of interventions. The incremental cost effectiveness ratio was used to select between scaling coverage of current interventions or the introduction of an additional intervention tool.Prevention, in the form of vector control, is highly cost effective and scale-up is prioritized in all scenarios. Prevention reduces malaria burden and therefore allows treatment to be implemented in a more cost-effective manner by reducing the strain on the health system. The chemoprevention measures  are additional tools that, provided sufficient funding, are implemented alongside treatment scale-up. Future tools, such as RTS,S vaccine, have impact in areas of higher transmission but were introduced later than core interventions.In a programme that is budget restricted, it is essential that investment in available tools be effectively prioritized to maximize impact for a given investment. The cornerstones of malaria control: vector control and treatment, remain vital, but questions of when to scale and when to introduce other interventions must be rigorously assessed. This quantitative analysis considers the scale-up or core interventions to inform decision making in this area.The online version of this article (10.1186/s12936-019-2755-5) contains supplementary material, which is available to authorized users. The World Health Organization (WHO) Global Technical Strategy for Malaria 2016–2030 (WHO-GTS) sets the goal of universal access to malaria prevention, treatment and diagnosis . The public private sector split remained constant as overall treatment coverage increased  and the private sector). The recipients of different interventions were assumed to be randomly correlated. The impact of prophylaxis as a result of treatments given to parasite negative cases was not modelled.LLINs were assumed to be distributed on a 3-year cycle. The modelled impact of LLINs is via both personal protection  and community protection (due to the killing effect of the insecticide). Nets were assumed to be effective and not impacted by insecticide resistance. LLIN access bands of 25, 50, 65, 75, 85, and 95% were considered. Due to the low and declining coverage of IRS in SSA [SMC was modelled as three rounds of sulfadoxine-pyrimethamine + amodiaquine administered to children aged 6 months to 5 years old. The doses were timed with respect to the peak in transmission: the first 1 month prior, the second with the peak and last 1 month post. SMC was evaluated at three high coverage bands , based on levels observed in the ACCESS-SMC implementation programme [RTS,S was considered at three high coverage bands  in medium- and high-transmission settings. High coverage reflects those consistently observed for routine immunization through the EPI . A four-When modelling packages of interventions, the model was run until a new equilibrium level of transmission was reached. The equilibrium was then used to quantify the different impact of intervention packages. This approach provides a standardized method which allows a like-for-like comparison of the impact of different intervention packages. It does however factor out dynamical shifts in transmission that occur before the new steady state is reached due to changes in the interventions used, their coverage and the timings at which changes occur.Fixed costs: These represent programme costs  and do not change with respect to coverage.Variable costs: These represent commodity costs  and increase linearly with coverage.All costs were estimated from the provider perspective and are summarized in Table c.Variable non-linear costs: These represent inefficiencies or increasing marginal costs as coverage increases. The functional form of non-linear costs was characterized by a fitted hill function .For LLINs and treatment there is evidence to suggest that there are increasing marginal costs associated with reaching increasingly high coverage. This non-linearity occurs for a number of reasons which include inefficiencies compounding at higher coverage  and thaTreatment costs for outpatient visits  and inpatient hospital stays (severe disease) were estimated using the WHO-CHOICE framework for all counties in SSA  Table 11. Costs LLIN costs were based on estimates of the relationship between the number of bed nets in a population and usage (or access) . This reA cost per dose of SP-AQ of $0.34 was used  Table 11. SMC coDistribution costs were also assumed to be the same as those for SMC. Whilst distribution costs may be shared with the EPI, implementing IPTi would present a departure from standard EPI methodology. As such, a conservative assumption was made that distribution costs would represent a stand-alone programme, such as ACCESS-SMC. Drug costs were rescaled to a value of $0.16 per dose of SP   based on fixed coverage bands (see above for the different interventions). Starting from zero coverage of all interventions, the first intervention to be implemented at the lowest coverage band was selected as that with the lowest ICER. At each subsequent step, either one of the existing interventions was increased to the next coverage band, or an additional intervention was introduced at the lowest coverage band, with the choice based on the option with the lowest ICER. This process was continued for each nominal budget until either all interventions were at maximum coverage or elimination was achieved.Results at each budget are presented as the mean coverage of each intervention across 20 uncertainty runs that include model parameter (intervention impact) and cost uncertainty. As interventions coverage was based on a small number of fixed discrete bands, uncertainty intervals for this metric could not be calculated.Figure Across all settings, LLINs are estimated to be the most cost-effective intervention and are therefore scaled first in the majority of simulation runs Fig. . This efFigure Figure Scale-up trends are broadly similar for the alternative outcome measure of clinical incidence only  is the most cost-effective option. With increasing transmission, scaling prevention also has an important role of reducing the burden of malaria upon the routine health system. As budgets increase to moderate levels, treatment coverage is scaled up. As the nominal budget is increased further, both LLINs and treatment are scaled up together. The cost of both LLINs and treatment is modelled to rise non-linearly at higher coverage. This reflects inefficiencies and challenges associated with attaining very high access and coverage of LLINs and treatment. As the budget is increased, the point at which an intervention is scaled is therefore sensitive to the relative inflection points in the cost curves. The switch from LLINs to treatment is, in part, driven by the increasing marginal costs of pushing LLIN access to mid to high levels. Likewise, attaining the maximum coverage of treatment is assumed to be increasingly expensive  and therefore the final steps where LLIN access and treatment coverage are both very high, are increasingly expensive. The impact and cost-effectiveness of scaling treatment, as well as being influenced by preventative interventions, is also a complex function of the immune dynamics of a population which impact the likelihood an infection is symptomatic and would instigate treatment-seeking. In this instance, it was assumed that all populations are equally likely to access either intervention. Spatial heterogeneities in the distributions of both malaria and available interventions could negatively impact outcomes . By assessing the impact of intervention packages at equilibrium in the model, temporal factors were omitted that may differ between interventions. These could include the time-scale for scale-up and associated impact. As such, policy decisions would also need to consider the feasibility and logistics of scaling any specific intervention to a target coverage. It is likely that the ability to execute an intervention package in a timely manner will also vary with the target coverage and where there is little to distinguish between two or more packages of interventions this may be a critical factor in decision making. As such the impact of timing and dynamic shifts in transmission is an important open research question in this field.Whilst prevention proved more cost-effective using this approach, there are ethical considerations surrounding providing treatment for all that fit with the broader picture of universal health coverage more generally . The goaIPTi and SMC are further cost-effective tools at the disposal of malaria control programmes in regions where their use is appropriate. Both chemoprevention options were introduced and scaled up after LLINs had reached mid to high levels of coverage, being best layered in addition to core vector control as opposed to replacing it. The setting was influential here also, as with increased intensity of transmission, cases become more concentrated within younger age groups . TherefoThe potential for the RTS,S vaccine to be included as an additional new tool in medium and high transmission areas was also examined. Whilst still cost-effective, and in line with previous work , the vacSurveillance, monitoring and evaluation activities were not included in this analysis despite the importance of these components to a malaria control programme. Other interventions, for example, behaviour change communication , could aThe outputs presented here demonstrate averaged approaches for generic transmission settings, aimed at informing decision-making in the general case. In practice there will be layers of context-specific information that will influence and shape the decision-making process. Malaria control programmes are faced with optimizing the impact of a single budget across an area with heterogeneous transmission, leading to a more complex optimization problem than presented here, and other more sophisticated approaches including, for example, simulated annealing  have beeThe ICER was used as the decision-step making tool in this analysis. It should be stated that this step-wise approach could potentially result in a non-optimal solution for any given budget. This is a result of taking a step-wise approach in a highly non-linear environment (both with respect to impact and cost) leading to restriction of the solution to local optima. The ICER does however benefit from simple interpretation and implementation.This study reiterates the importance of a combination of prevention, via vector control (here LLINs), and treatment as the cornerstone of malaria control in SSA. Additional currently recommended interventions (chemoprevention) and tools in development  will be valuable in those many areas where vector control and treatment are themselves not enough to bring about a transition to elimination. When resources are limited the prioritization of the use of available tools can help to maximize the impact of available finances, reducing malaria morbidity and mortality in a cost-effective manner.Additional file 1. Additional information.Additional file 2: Figure S1. Cost-effective prioritisation of LLINs and treatment. The average cost-effective scale-up of access to LLINs (blue bars) and coverage of treatment (red bars) for A) low (baseline PfPr2-10: 10%), B) medium (baseline PfPr2-10: 30%) and C) high (baseline PfPr2-10: 60%) seasonal transmission settings.Additional file 3: Figure S2. Cost-effective prioritisation of LLINs and treatment: Cases only outcome. The average cost-effective scale-up of access to LLINs (blue bars) and coverage of treatment (red bars) for A) low (baseline PfPr2-10: 10%), B) medium (baseline PfPr2-10: 30%) and C) high (baseline PfPr2-10: 60%) perennial transmission settings.Additional file 4: Figure S3. Cost-effective prioritisation of LLINs, treatment and IPTi or SMC: Cases only outcome. The average cost-effective scale-up of access to LLINs (blue bars) and coverage of treatment (red bars) with IPTi (light green bars) in perennial transmission settings or SMC (purple bars) in seasonal transmission settings. Scale-up is shown for A, C) medium (baseline PfPr2-10: 30%) and B, D) high (baseline PfPr2-10: 60%) transmission settings.Additional file 5: Figure S4. Cost-effective prioritisation of LLINs, treatment and the RTS,S vaccine: Cases only outcome. The average cost-effective scale-up of access to LLINs (blue bars), coverage of treatment (red bars) and the RTS,S vaccine (orange bars) for A) medium (baseline PfPr2-10: 30%) and B) high (baseline PfPr2-10: 60%) perennial transmission settings.Additional file 6: Figure S5. Cost-effective prioritisation of LLINs and treatment: Cases and deaths outcome. The average cost-effective scale-up of access to LLINs (blue bars) and coverage of treatment (red bars) for A) low (baseline PfPr2-10: 10%), B) medium (baseline PfPr2-10: 30%) and C) high (baseline PfPr2-10: 60%) perennial transmission settings.Additional file 7: Figure S6. Cost-effective prioritisation of LLINs, treatment and IPTi or SMC: Cases and deaths outcome. The average cost-effective scale-up of access to LLINs (blue bars) and coverage of treatment (red bars) with IPTi (light green bars) in perennial transmission settings or SMC (purple bars) in seasonal transmission settings. Scale-up is shown for A, C) medium (baseline PfPr2-10: 30%) and B, D) high (baseline PfPr2-10: 60%) transmission settings.Additional file 8: Figure S7. Cost-effective prioritisation of LLINs, treatment and the RTS,S vaccine: Cases and deaths outcome. The average cost-effective scale-up of access to LLINs (blue bars), coverage of treatment (red bars) and the RTS,S vaccine (orange bars) for A) medium (baseline PfPr2-10: 30%) and B) high (baseline PfPr2-10: 60%) perennial transmission settings.Additional file 9: Figure S8. Example of posterior draws for the non-linear component of treatment delivery costs. The cost multiplier is applied to the cost of treating a clinical case to provide an estimate of distribution costs that increase non-linearly as coverage reaches very high levels. Assumes that distribution at baseline (in the absence of non-linear effects) is approximately 15% of the cost per clinical case.


Owing to safety concerns or insufficient efficacy, few drug candidates are approved for marketing. Drugs already on the market may be withdrawn due to adverse effects (AEs) discovered after market introduction. Comprehensively investigating the on-/off-target effects of drugs can help expose AEs during the drug development process. We have developed an integrative framework for systematic identification of on-/off-target pathways and elucidation of the underlying regulatory mechanisms, by combining promoter expression profiling after drug treatment with gene perturbation of the primary drug target. Expression profiles from statin-treated cells and HMG-CoA reductase knockdowns were analyzed using the framework, allowing for identification of not only reported adverse effects but also novel candidates of off-target effects from statin treatment, including key regulatory elements of on- and off-targets. Our findings may provide new insights for finding new usages or potential side effects of drug treatment. From pre-clinical tests to clinical trials, studies of new drug candidates assess both efficacy and safety, and comprehensively screen for adverse effects3. In the pre-clinical testing stage, the pharmacokinetics and pharmacodynamics of drugs are characterized by in vitro and in vivo studies, including absorption, distribution, metabolism, excretion, and persistence of pharmacological effects. In addition, drug safety is evaluated for unexpected and toxic effects on target tissues, carcinogenicity and mutagenicity. Once the pre-clinical tests ensure that the drug consistently produces the desired effects, the safety and dosing of the drug is determined through testing in cultured cells, animal models and healthy human volunteers in clinical phase I trials. In clinical phase II and III trials, the efficacy and safety of the drug are assessed on small and large cohorts of patients having the targeted disease. Post-marketing surveillance, known as phase IV, monitors adverse effects from long-term usage4. Several examples of adverse effects leading to withdrawal of drugs from the market have been reported. For example, Dextropropoxyphene that was patented in 1955 and used for analgesia, was withdrawn in recent years because of increasing risk of heart attacks and stroke5. Early detection of adverse effects (AEs) to ensure drug safety is important to prevent the harming of patients and to reduce the cost of drug development.The process of drug development is costly and time-consuming7. The potential to detect AEs in pre-clinical tests or clinical trials is limited by the number of participating patients, the duration of the studies and heterogeneity of populations8. In phase IV trials, post-marketing surveillance to monitor adverse events in real time is also challenging due to the passiveness of pharmacovigilance (drug safety) methods for collecting voluntary submissions through spontaneous reporting systems (SRSs) or mandatory submissions from healthcare center or pharmaceutical companies. Based on data from SRSs, several data-mining-based pharmacovigilance algorithms have been developed to perform disproportionality analyses to discover unexpected and adverse effects of drugs9. The results from these algorithms may be biased depending on the source of data, sampling variance and reporting bias. Even the multi-item gamma Poisson shrinker (MGPS) method that corrects data source bias has issues with high rates of false positives and negatives yet to be solved10. Recently, network-based methods have been developed that integrate chemical data with biological data sources for construction of AE networks, identifying putative mechanisms of AEs11.Many efficacious drugs have off-target effects, for example multi-kinase inhibitors for cancer therapies, and the off-target effect may cause adverse effectset al. established the first database, Connectivity Map (CMAP) collecting gene-expression profiles from cultured human cells treated with 1,300 bioactive small molecules and providing interfaces for pattern-matching to mine these data. Using CMAP, there are several studies indicating novel usages for previously developed drugs12.Since the established algorithms for predicting AEs rely on reported data, developing an approach that can elucidate on- and off-target effects at the pre-clinical stage could allow for early identification of potential AEs, thus reducing the cost and time for drug development. In addition, comprehensive prediction of on- and off-target effects may be useful for drug repurposing, where new indications for existing drugs are identified. Drug repurposing has an advantage over the development of novel drugs, in that tedious and costly processes of drug development, especially for the safety concerns, may be bypassed. In 2006, Lamb 13, and may also influence intracellular signaling pathways and immune processes14. Several studies have suggested that statins may have potential as cancer therapy in human malignancies, including liver cancer16, breast cancer18, and leukemia20. In contrast, some studies revealed that statins increase cancer risk of prostate cancer22. In addition, there are other adverse effects of statins that have been identified in recent years, for example, increased risk of early-onset of type II diabetes23. However, the details of the mechanisms of these unexpected effects, whether positive or negative, are still not clear. Applying our framework to treatment of three different cell types with four widely used statins, we identify significantly associated pairs of transcriptional factors and pathways, and infer regulatory networks to elucidate the regulation of on-/off-target pathways. We show that our framework provides new insights about the effect of the drugs under study, and helps identification of new usages or potential side effects.In this study, we establish an integrative framework to dissect on-/off-target effects of drug treatment at the molecular level, to help future identification of potential adverse effects. Using Cap Analysis of Gene Expression (CAGE) as the profiling technology, we measure the transcriptome activity at promoters in a quantitative and qualitative way, allowing for identification of the promoters/genes affected by the treatment, as well as high resolution identification of the regulatory elements involved in the regulation of the observed changes. We selected statins, commonly used for lowering cholesterol levels, as an example to evaluate our framework. Statins, inhibitors of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase, have been majorly used for anti-hypercholesterolemiaThe general framework is described in Fig. 14. The 5′-capped RNA molecules are captured as CAGE tags, which reflect the transcription start sites and expression level of transcribed RNA molecules. The CAGE tags were mapped and annotated to the promoter regions identified in the FANTOM 5 project24  is the main target of statins, comparing the CAGE expression profiles between statin-treated and HMGCR knockdown cells allowed for investigation of putative on- and off-target effects of statins. We applied an ANOVA model to identify the differentially expressed promoters (DEPs) in statin-treated cells at two time-points (6 hours and 48 hours) after treatment of each statin. The DEPs were also identified in the two HMGCR knockdown experiments. Subsequently a step-wise filter was applied to define on- and off-target effects , as well as knockdowns of 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG- CoA), the main target of statin treatment. The human cultured cell lines selected were HepG2 (liver cancer cells), MCF-7 (breast cancer cells) and THP-1 cells , which are related to statin-targeting organs or pleiotropic effects were found to be shared between HepG2 and MCF-7 cells , which was up-regulated in most of the statin-treated cells except for Rosuvastatin-treated HepG2 cells, is also involved in the cholesterol biosynthesis pathway. In addition to these two on-target responders, FDFT1 was identified as an on-target responder common to MCF-7 and THP-1 cells. This gene encodes a membrane-associated enzyme involved in the mevalonate pathway in cholesterol biosynthesis. No up-regulated on-target responder common to HepG2 and THP-1 cells was identified and there were no down-regulated on-targets common to all three cell lines.Surprisingly few promoters of genes could be consistently identified as common on-target responders, whether at the statin level or cell level  in the different conditions. The expression profiles in the three cell lines after HMGCR-knockdown or 6/48-hours statin treatment were analyzed by GSEA to identify significantly altered gene-signatures reflecting on- and off-target effects. Following the same rules identifying on- and off-target responders defined at the gene level, the on- and off-target pathways were identified based on the results of GSEA, by comparing gene-signatures enriched in statin-treated cells with signatures from HMGCR knockdown cells. The whole enrichment results are shown in Supplementary Fig. The observation that few common on- or off-target responders could be identified in the three cell lines or across the four statins indicated that the molecular response may be cell-type dependent and statin specific at the gene level. We hypothesized that the response may be more similar at the systemic level, and proceeded with a gene sets based comparison approach in order to investigate the on- and off-target effects at the pathway level. Gene-set enrichment analysis (GSEA)HMGCR knockdown cells as well as in most statin-treatments ” pathway was identified as on-target effect in HepG2 and MCF7 cells, but off-target effect in THP1 cells. There are several studies suggesting that statins could be used as SLE therapy, independent of cholesterol regulation32. In our GSEA results, the “Systemic lupus erythematosus (SLE)” pathway was significantly down-regulated in three cell lines after statin treatment .In order to further examine our framework’s capability of identifying adverse or pleiotropic effects, we focused the analysis on disease pathways represented in KEGG. Out of 36 disease pathways identified to be on- or off-targets, 15 had various relations to cancer, containing a multitude of biological processes such as multiple signaling pathways, regulation of apoptosis, cell adhesion, in each pathway. We focused on the remaining 21 pathways, covering cardiovascular diseases, endocrine and metabolic diseases, immune diseases, infectious diseases, and neurodegenerative diseases. 19 of 21 pathways were significantly altered as on-/off-target effects of statins 35. Accordingly, the “Type II diabetes mellitus” pathway was up-regulated as off-target effect in Rosuvastatin-treated MCF7 cells as well as in all statin treatments of THP1 cells. Furthermore, we found that statin treatment signatures were inversely correlated to an anti-type II DM drug, Pioglitazone, which is a type of thiazolidinedione medicine (TZD) that increases the sensitivity of insulin. The prevention of neuron degeneration diseases has been determined as off-target effects of statins in previous studies38; in line with this we detected several pathways related to neuron degeneration diseases down-regulated by statin treatment as well. In contrast, the pathway of amyotrophic lateral sclerosis (ALS) in Rosuvastatin-treated MCF7 cells was significantly up-regulated, lending support to previously shown associations between statins and ALS-like syndrome in clinical studies39 and further supported in silico by the observation that the signatures of Riluzole, which is used to treat amyotrophic lateral sclerosis (ALS), are inversely correlated to our statin-signatures in statins-treated cells. There are previous studies showing decreasing risks of heart failure from statin treatments by lowering cholesterol levels41, but there are also studies indicating that statin could cause heart disease42. The cardiovascular disease pathways were up-regulated in statin-treated THP1 cells as well. In addition, the signatures of drug used for heart disease, Enalapril, showed inverse correlation to several statin signatures.In each disease category, we could confirm at least one pathway through in silico validation (Methods) or literature support Fig. . One of Taken together, the results suggest that our approach could identify several previously reported adverse effects of statins, and provide putative novel adverse effects or novel indications of statins that may be followed up in future investigations.43 was used to identify the transcriptional regulatory dynamics associated with changes in expression after treatment with different statins in the three types of cells. In MARA, motif activities are modeled as a function of the number of transcription factor binding sites in the promotor regions and fitted to the expression levels of the promoters. After identifying the differentially activated motifs of transcriptional factors (TFs), we performed Fisher’s exact test to analyze the associations between activated motifs and GSEA-enriched pathways, based on the genes belonging to the pathways and the putative targets of each motif according to MARA. In total, 121,267 motif-pathway pairs were tested, with 778 pairs significantly  associated. Analyzing each cell type separately, 278, 191 and 309 significant TF-pathway pairs (92 unique TFs) were identified in HepG2, MCF-7 and THP-1 cells respectively.In order to further elucidate the effects of statin treatment, we attempted to identify the key regulatory elements of the specific biological processes induced by statins. Taking advantage of the fact that CAGE pinpoints the exact locations of promotor activity which allows for precise identification of the cis-regulatory elements (motifs) implicated in regulating the promoter activity, Motif Activity Response Analysis (MARA)FOXD1/2, PATZ1, and TLX2, were associated with the reported adverse effect, “Type II diabetes mellitus (Type II DM)”  down-regulated and the results correspond to the down-regulation of “Systemic lupus erythematosus (SLE)” pathway in most of the statin-treated cells  in SLE patients45. The over-expression of TBP has also been determined in SLE patients46. The down-regulation of the activity of these TFs suggest a potential mechanism for how statins suppress the SLE pathway. The other on-target pathway, “Steroid biosynthesis”, was associated with KLF4 in MCF7 and THP-1 cells only.Based on our findings, the highly activated motifs of transcription factors JUN, HIC1, NFKB1_REL_RELA, and TFAP2B, putatively controlling programmed cell death of statin-treated cells as off-target effects , and Motif Activity Response Analysis (MARA) to identify significant motif-pathway pairs contributing to the on- and off-target effects identified by comparing statin treatment and HMGCR (the primary target of statins) knockdown profiles. Off-target effects generally occur in any perturbation experiment. Therefore, we chose a conservative approach and created the on-target effective gene sets in this study by using negative control siRNA and two HMGCR-specific siRNAs, which minimizes the non-specific effects of siRNA treatment as well as the siRNA sequence-specific off-target effects. We observed that a very small amount of genes could be defined as on-target effect genes using these strict criteria. This suggests that HMGCR is an ideal drug target for cholesterol lowering, with minimum effect on other cellular functions.For promoters of genes, we rarely identified common on- or off-target responders, neither across statins nor cell lines. Apart from a set of key genes in cholesterol biosynthesis, , there were no other differentially expressed promoters shared in at least two cell lines. In addition, we observed that statin treatment resulted in significant changes in a large amount of off-target genes. This is unlikely to be due to incomplete siRNA KD efficiency, since we rarely observed “off-target” genes common to all statin treatments. Thus, those off-target effects are likely to reflect differences in molecular structure of the four statins used here, where each statin may weakly associate with additional other cellular molecules or proteins. Because expression of those molecules may be variable between different cell-types, it is beneficial to use multiple cell-types for detection of off-target effects in this approach.In contrast, there were many pathways shared among different statins and cell lines, such as the on-target effects up-regulation of “Steroid biosynthesis” and “Terpenoid backbone biosynthesis pathway” for negative feedback of statins, and off-target effects including the systemic lupus erythematosus (SLE) pathway which is down-regulated under statin treatment. In addition, the most well-known AE, the “Type II diabetes mellitus” pathway, was identified as an up-regulated off-target. Our finding suggests that statin treatment targets key pathways similar in different cell types rather than specific genes. Comparing our statin-induced gene-signatures with pre-compiled signatures from the LINCS database further validated our putatively identified on-/off-targets of statins. Signatures from drugs (RS-17053 and Niguldipine) that can decrease LDL and triglycerides and increase HDL were significantly enriched. In addition, several anti-cancer drugs signatures in the LINCS database were significantly associated as well. Furthermore, Type II DM therapies and PARP inhibitors showed inverse correlation with statin signatures. These results not only support our enrichment results but also show that our framework can dissect on- and off-target effects of drugs, and may be used for repositioning to other usages.48 where they have roles in the regulation of immune responses and inflammation. FoxD1 has been reported to play a role in the induction of plasminogen activator inhibitor-1 (PAI-1) to regulate the vascular function and modulate thrombosis, inflammation, and the extracellular matrix48. PATZ1 binds to DNA and plays an important role in chromatin modeling and since there are few genetic variants associated with T2DM , epigenetic regulation has been proposed to play significant role in T2DM instead49.Although the framework can in principle be applied to any expression measurement technology such as RNA-seq or microarrays, the choice of CAGE allowed us to achieve a more accurate identification of regulators responsible for the on-/off-target effects using MARA. Our framework not only identified key regulators which have been previously reported for known off-target effects of statins, such as E2F1 and POU2F1 for systemic lupus erythematosus and JUN and NFKB1 for anti-cancer-related pathways, but also novel regulators  that may play important roles in statin-increased risk for Type II diabetes mellitus (T2DM). Some FOX (forkhead) transcription factors are associated with diabetes-related vascular diseaseUsing preclinical data can not only reduce the cost and time for drug development but also prevent patients from serious adverse effects (AEs) in the early stages of drug treatment. However, a method that systematically uses preclinical data to identify putative AEs is still lacking. The framework presented here may aid in prioritizing among candidates when some of them may have suggested AEs at the molecular level.52. There are three major classes of HDACs. The pan inhibitors inhibit the enzyme activity for all kinds of HDACs, whereas identifying class-specific HDAC inhibitors or dissecting the on- and off-target effects of specific classes of HDACs are still unmet clinical needs. Our study shows the capability of identifying unexpected effects including potential adverse effects by preclinical data from human cultured cell lines. Applying this framework in early stage of drug development may filter out the drug candidates with potential AEs to reduce time and costs.Based on a similar experimental design, our framework can be applied to other drugs in the future. For example, the pan histone deacytelatse (HDAC) inhibitors have been used as anti-cancer drugs or neurodegenerative diseaseHMGCR gene  and negative control siRNA  were transfected using Lipofectamine RNAiMAX Transfection Reagent (Thermo Fisher Scientific) and incubated for 48 h. The HMGCR knockdown efficiency for HSS104864 and HSS179267 was 80% and 81% (HepG2), 42% and 52% (MCF-7) and 77% and 70% (THP-1), respectively. After the treatments, total RNA was extracted from the cells using RNeasy kit (Qiagen) and was subjected to the CAGE analysis using the nAnT-iCAGE protocol. Triplicate data was produced for statin and siRNA treatments, and 6 replicate data for control of the statin  and negative control siRNA treatments. CAGE libraries were sequenced on Illumina HiSeq. 2000. CAGE data is available at Gene Expression Ominbus (accession number GSE134817).HepG2 and MCF-7 cells were cultured in DMEM high glucose (Gibco) supplemented with 10% inactivated fetal bovine serum (Gibco), 100 U/mL penicillin, 100 μg/mL streptomycin (Gibco) and 1 mM sodium pyruvate (Gibco). THP-1cells were cultured in RPMI-1640 (Gibco) supplemented with 10% inactivated fetal bovine serum (Gibco), 100 U/mL penicillin and 100 μg/mL streptomycin (Gibco). In the statin treatment, Atorvastatin, Fluvastatin, Rosuvastatin and Simvastatin were added to the culture medium at the final concentration of 30 μM for MCF-7 and THP-1 and 10 μM for HepG2 for 6 and 48 h. In the siRNA treatment, 2 siRNAs against 53 for processing CAGE tags generated. Briefly, bwa and delve was used for mapping with default parameters. We used the first position of the start of reads mapped on the positive strand, and the last position of the end of reads mapped on the negative strand, as the CAGE tag positon. CAGE tags were mapped to robust decomposition peak identification (DPI) clusters defined and annotated in FANTOM 5 database24 using the intersectBed function of bedtools54. The expression profile of each sample was merged to a DPI clusters-by-sample table as input for edgeR55. The read counts of tag-clusters were normalized by “relative log expression (RLE)” method and the normalized expression table was subsequently filtered to discard tag-clusters expressed at less than five counts per million (CPM) in all samples of each cell line. In addition, the voom function from limma56 was used to transform RLE-normalized CPM values to fit a normal distribution. The voom-transformed data was used for gene-set enrichment analysis (GSEA) and motif activity analysis. For GSEA, the CPM of tag-clusters belonging to the same gene annotated in the FANTOM 5 database was summarized to represent expression levels at the single gene level. Log2-transformed CPM profiles were applied to Partek® Genomics Suite® software, version 6.6 Copyright ©; 2016 Partek Inc., St. Louis, MO, USA. for batch-removal, identification of differentially expressed tag-clusters (DETs) by ANOVA, and data visualization. For identification of DETs a false discovery rate  threshold was used (≤5%).We used the MOIRAI pipeline28. An advantage of using GSEA for analyzing drug response is that not only strongly regulated genes, but also the trends of gene expression in pathways, are considered. The GSEA software was downloaded from the Broad Institute website and installed locally and (http://www.broad.mit.edu/gsea/). This algorithm uses a normalized Kolmogorov-Smirnov statistic to identify gene-signatures that are significantly associated with gene expression profiles of distinct phenotypes. An enrichment score (ES) of specific gene-signature are calculated for each phenotype; a normalized ES (NES), which is divided by the maximum ES in individual enrichment analysis, is used to compare the same gene-signature in different experiments. The biological knowledge base used in GSEA is the Molecular Signatures Database (MSigDB) v5.0. First we merged the expression values of promoters belonging to the same gene as the summarized value to represent the expression level of each gene. A ranked gene expression profile was generated based on the built-in signal-to-noise algorithm. GSEA identifies the most represented gene-patterns where the p-value is generated from permuting the members in a given gene-signature 1000 times. For the sensitivity and specificity of the enrichment analysis, the sizes of individual gene-signatures were limited from 5 to 500. Based on this filtering, 168 of 187 KEGG pathways remained for enrichment tests. The threshold of significance that was used followed the recommendation from GSEA manual, with p-value ≤ 0.05 or FDR ≤ 20%.GSEA aims to identify whether members of a gene-signature predefined by prior biological knowledge tend to be distributed at the top or bottom of a sorted gene list based on expression profiles associated to distinct phenotypes, such as case vs. control or treated vs. untreatedhttp://lincscloud.org) in the LINCS (Library of Network-Based Cellular Signatures) database. In order to verify the specificity of each signature from different cell lines treated with statins, 50 randomly selected up- and down-regulated gene-signatures from the human whole transcriptome were also generated 100 times to query the database for comparison of the results from random queries with the original signatures. As following the recommendation from lincscloud website, we used the mean connectivity score (score_best4) across the four cell lines in which the pertubagen connected most strongly to the querying signatures to represent the standard of similarity. Then, one-sample T-test was performed to examine the significance between the score of specific drug from our querying signature and random data-sets. In total, there are 3,096 drug-signatures in LINCS database that were compared. The signatures are defined as significantly enriched by score_best4 ≥ 80 or score_best4 ≤ −80 with FDR ≤ 5%.In order to assess the validity of identified on-/off-target pathways, we selected the top 50 and bottom 50 genes from each combination of statin and cell type, sorted by the statistical significance of their differential expression as signatures to compare with the 3,096 drug-induced signatures that could be queried by lincscloud  defined as central motifs by FANTOM 5 with respect to the transcriptional start site (TSS). Second, the putative binding sites of TFs were associated with FANTOM 5 promoter regions. The threshold on the number of predicted TFBSs for each motif defaults to 150. In final, the motif activities were calculated. This step will generate a single file containing the motif activities in each condition, their standard deviations, and their overall Z-scores. The ANOVA was performed to identify differentially activated motifs under 48-hours treated vs. control samples. Based on the information of putative targets on promoters, we performed Fisher’s exact test to examine the number of genes which belongs to the specific pathway, and identified cases where the stargeting by a specific TF was significantly higher than for the whole transcriptome background. There were 778 of 121,267 motif-pathway pairs significantly associated at false discovery-rate < 0.05).In brief, according to the motif occurrence in the regions −300 to +100 base pairs (bp) from the peak of FANTOM 5 robust DPI clusters, MARA describes the ability of motifs to significantly explain the variance in expression between Supplementary InfoDataset 1


Hydrothorax due to pleuroperitoneal communication (PPC) is a rare complication of continuous ambulatory peritoneal dialysis (CAPD). Approximately 50% of the patients need to convert to hemodialysis.99mTc-macro-aggregated albumin was administered into the peritoneal cavity with dialysate, and a leakage point of the dialysate into the right pleural cavity was detected. He was diagnosed with PPC and underwent video-assisted thoracic surgery (VATS). The hole was closed by direct suturing and reinforced by a pedicled latissimus dorsi muscle flap (LDM). The patient resumed CAPD 7 weeks later and had no recurrence of the right hydrothorax.A 65-year-old man with chronic renal failure due to membranoproliferative glomerulonephritis underwent CAPD. Seven months after starting CAPD, a chest X-ray showed a right hydrothorax. For performing radioscintigraphy, VATS was a useful method for treating PPC. A pedicled LDM flap is an effective material for preventing the recurrence of PPC. Hydrothorax due to pleuroperitoneal communication (PPC) is a rare complication of continuous ambulatory peritoneal dialysis (CAPD), and its incidence rate is estimated at approximately 1.6% of all patients undergoing CAPD [99mTc-macro-aggregated albumin (99mTc-MAA) was administered into the peritoneal cavity with dialysate, and 240 min later, a leakage point of the dialysate into the right pleural cavity was detected  were placed at both places, and a 30°, 10-mm thoracoscope was inserted at the sixth intercostal space of the anterior axillary line. By carefully inspecting the diaphragm with the thoracoscope, the hole was detected at the right central tendon of the diaphragm . For avoiding liver damage by suturing, the diaphragm was pulled sufficiently and then sutured. The central tendon around the reinforcement was covered with a sheet of absorbable polyglycolic-acid (PGA) sheet    was inserted in the right pleural cavity, and a 15-Fr silicone drain  was inserted in the subcutaneous. The chest silicone drain was removed on the fourth postoperative day, because the amount of pleural effusion decreased to less than 100 mL. The subcutaneous silicone drain was removed on the eighth postoperative day, because the amount of drainage decreased. The patient was discharged on the tenth postoperative day. The patient resumed CAPD 7 weeks later, and no recurrence of the right hydrothorax was observed for 14 months.A 65-year-old man with chronic renal failure due to membranoproliferative glomerulonephritis underwent CAPD. Seven months after starting CAPD, he developed dyspnea. A chest X-ray and computed tomography showed the right hydrothorax Fig. a, b. PPCted Fig. c. SurgicHydrothorax due to PPC is a rare but a well-known complication of CAPD . When hyAs the onset cause of PPC, several studies speculate that PPC is speculated to be induced by (1) the defects of diaphragm due to abnormal intraperitoneal pressure, (2) congenital or traumatic diaphragm defects, 3) defects or laceration of blebs in the fragile region of the diaphragm, and (4) migration through lymphatic vessels  defects . In this99mTc-MAA radioscintigraphy from peritoneal cavity to pleural cavity. In this case, the preoperative diagnosis of PPC was obtained by using 99mTc-MAA radioscintigraphy.PPC is diagnosed by pleural fluid examination and the transition of Although approximately 50% of the patients need to convert to hemodialysis, most of the patients still need to continue CAPD for social reasons . Either In previous reports, PGA sheets, fibrin glue, and pericardial fat pad tissues are used to reinforce the diaphragm for treating PPC , 9. HoweVATS was an effective method for treating PPC, and not only suturing the fistula but also reinforcing the responsible lesion of the diaphragm for preventing a recurrence of hydrothorax.