Knowledge Of Drug Interactions Research Articles

The development and impact of an app for a smart drug interaction reminder system.

Improved access to media and medical knowledge has elicited stronger public health awareness. This study developed a smart drug interaction reminder system for patients to increase knowledge and reduce nurse workload. This study used a single-group pre-test/post-test design and applied mining techniques to analyze the weight and probability of interaction among various medicines. Data were collected from 258 participants at a teaching hospital in northern Taiwan using convenience sampling. An app was used to give patients real-time feedback to obtain access to information and remind them of their health issues. In addition to guiding the patients on medications, this app measured the nurses' work satisfaction and patients' knowledge of drug interaction. The results indicate that using information technology products to assist the app's real-time feedback system promoted nurses' work satisfaction, improved their health education skills, and helped patients to better understand drug interactions. Using information technology to provide patients with real-time inquiring functions has a significant effect on nurses' load reduction. Thus, smart drug interaction reminder system apps can be considered suitable nursing health education tools and the SDINRS app can be integrated into quantitative structure-activity relationship intelligence in the future.

PillHarmonics: An Orchestrated Pharmacogenetics Medication Clinical Decision Support Service.

Pharmacogenetics (PGx) is increasingly important in individualizing therapeutic management plans, but is often implemented apart from other types of medication clinical decision support (CDS). The lack of integration of PGx into existing CDS may result in incomplete interaction information, which may pose patient safety concerns. We sought to develop a cloud-based orchestrated medication CDS service that integrates PGx with a broad set of drug screening alerts and evaluate it through a clinician utility study. We developed the PillHarmonics service for implementation per the CDS Hooks protocol, algorithmically integrating a wide range of drug interaction knowledge using cloud-based screening services from First Databank (drug-drug/allergy/condition), PharmGKB (drug-gene), and locally curated content (drug-renal/hepatic/race). We performed a user study, presenting 13 clinicians and pharmacists with a prototype of the system's usage in synthetic patient scenarios. We collected feedback via a standard questionnaire and structured interview. Clinician assessment of PillHarmonics via the Technology Acceptance Model questionnaire shows significant evidence of perceived utility. Thematic analysis of structured interviews revealed that aggregated knowledge, concise actionable summaries, and information accessibility were highly valued, and that clinicians would use the service in their practice. Medication safety and optimizing efficacy of therapy regimens remain significant issues. A comprehensive medication CDS system that leverages patient clinical and genomic data to perform a wide range of interaction checking and presents a concise and holistic view of medication knowledge back to the clinician is feasible and perceived as highly valuable for more informed decision-making. Such a system can potentially address many of the challenges identified with current medication-related CDS.

Assessment of potential drug–drug interaction knowledge, attitude, and practice among Egyptian hospital and community pharmacists: a cross-sectional multicenter study

BackgroundPharmacists have an important role in preventing prescribing errors and providing appropriate information. They can detect potential drug–drug interactions (DDIs), which are associated with a more extended hospital stay and higher medical costs that lead to substantial financial burdens on healthcare systems. This study aimed to evaluate and assess the knowledge of community and hospital pharmacists toward drug–drug interaction and their attitude and motivation to find DDI information, in addition to identifying the pharmacist factors affecting this knowledge. A cross-sectional multicenter study was conducted using a self-administered questionnaire. Nineteen drug pairs, that are common in clinical practice, were evaluated. This study aimed to evaluate and assess the knowledge of community and hospital pharmacists toward drug–drug interaction and their attitude and motivation to find DDI information, in addition to identifying the pharmacist factors affecting this knowledge.ResultsA total of 4363 pharmacists (2260 community pharmacists and 2103 hospital pharmacists) have completed the survey. The participants' knowledge of DDIs was 58.25%, and there was no significant difference in pharmacist knowledge between community and hospital pharmacists (p = 0.834). The highest correct answer was for sildenafil and isosorbide mononitrate pair 78.8%. The most used source of information was the internet or mobile applications, 47.1%. Participants who always considered PDDIs while prescribing detected more drug interactions than those who did not (p = 0.001).ConclusionAccording to the findings of this study, community and hospital pharmacists had comparable knowledge of DDIs. However, before dispensing uncommon prescriptions, they should consult evidence-based drug information resources and DDI software to identify potential drug interactions.

Cardiac Myosin Inhibitors: Expanding the Horizon for Hypertrophic Cardiomyopathy Management.

To review the current literature on the efficacy and safety of cardiac myosin inhibitors (CMIs) for the treatment of hypertrophic cardiomyopathy (HCM). A literature search was conducted on PubMed from origin to April 2023, using the search terms "MYK-461," "mavacamten," "CK-3773274," and "aficamten." Studies were limited to English-based literature, human subjects, and clinical trials resulting in the inclusion of 13 articles. ClinicalTrials.gov was also used with the same search terms for ongoing and completed trials. Only phase II and III studies were included in this review except for pharmacokinetic studies that were used to describe drug properties. CMIs enable cardiac muscle relaxation by decreasing the number of myosin heads that can bind to actin and form cross-bridges. Mavacamten, the first Food and Drug Administration (FDA)-approved drug in this class, has been shown to improve hemodynamic, functional, and quality of life measures in HCM with obstruction. In addition, aficamten is likely to become the next FDA-approved CMI with promising phase II data and an ongoing phase III trial expected to release results in the next year. CMIs provide a novel option for obstructive hypertrophic cardiomyopathy, particularly in those not suitable for septal reduction therapy. Utilization of these agents requires knowledge of drug interactions, dose titration schemes, and monitoring parameters for safety and efficacy. CMIs represent a new class of disease-specific drugs for treatment of HCM. Cost-effectiveness studies are needed to delineate the role of these agents in patient therapy.

A prospective observational study on drug-drug interactions in chronic kidney disease patients

Aim: To review Drug-Drug interactions among CKD patients. Methodology: A concurrent conciliation study was performed on CKD patients with drug-drug interactions admitted in and around tertiary government and corporate hospitals, Visakhapatnam, Andhra Pradesh, India. Information regarding gender, and variety of drug interactions as mild, moderate, and major were recorded in the standard questionnaire. Result: Overall 150 prescriptions were included after excluding missing data. The impact of concomitant disease in causing drug-drug interactions in 150 prescriptions them were Hypertension -35%, Diabetes Mellitus-19%, Hyperlipidemia-8%, Asthma- 7%, Seizures- 6%, Thyroid -5%, Congestive Heart Failure- 1%. Total of 201 interactions were revealed of which 15 were major interactions, 139 were moderate interactions, 47 were minor interactions. Conclusion: By assessing the above data it absolutely was concluded that the patient should possess sound knowledge of drug interactions before employing a selected drug or two or more drugs at the identical time so as to possess identical medication.

Signals of Muscle Relaxant Drug Interactions Associated with Unintentional Traumatic Injury: A Population-Based Screening Study.

Use of muscle relaxants is rapidly increasing in the USA. Little is understood about the role of drug interactions in the known association between muscle relaxants and unintentional traumatic injury, a clinically important endpoint causing substantial morbidity, disability, and death. We examined potential associations between concomitant drugs (i.e., precipitants) taken with muscle relaxants (affected drugs, i.e., objects) and hospital presentation for unintentional traumatic injury. In a series of self-controlled case series studies, we screened to identify drug interaction signals for muscle relaxant + precipitant pairs and unintentional traumatic injury. We used Optum's de-identified Clinformatics® Data Mart Database, 2000-2019. We included new users of a muscle relaxant, aged 16-90years, who were dispensed at least one precipitant drug and experienced an unintentional traumatic injury during the observation period. We classified each observation day as precipitant exposed or precipitant unexposed. The outcome was an emergency department or inpatient discharge diagnosis for unintentional traumatic injury. We used conditional Poisson regression to estimate rate ratios adjusting for time-varying confounders and then accounted for multiple estimation via semi-Bayes shrinkage. We identified 74,657 people who initiated muscle relaxants and experienced an unintentional traumatic injury, in whom we studied concomitant use of 2543 muscle relaxant + precipitant pairs. After adjusting for time-varying confounders, 16 (0.6%) pairs were statistically significantly and positively associated with injury, and therefore deemed signals of a potential drug interaction. Among signals, semi-Bayes shrunk, confounder-adjusted rate ratios ranged from 1.29 (95% confidence interval 1.04-1.62) for baclofen + sertraline to 2.28 (95% confidence interval 1.14-4.55) for methocarbamol + lamotrigine. Using real-world data, we identified several new signals of potential muscle relaxant drug interactions associated with unintentional traumatic injury. Only one among 16 signals is currently reported in a major drug interaction knowledge base. Future studies should seek to confirm or refute these signals.

South African healthcare workers' knowledge of dolutegravir's drug-drug interactions in the first year of its rollout: a cross-sectional online survey.

IntroductionIn December 2019, dolutegravir‐based treatment was recommended as first‐line antiretroviral therapy (ART) in South Africa. Dolutegravir has clinically significant interactions with several commonly used drugs, such as rifampicin, metformin and cation‐containing medicines. National guidelines detail these interactions and how to manage them. While previous international studies have shown low healthcare worker knowledge of drug–drug interactions, there is a paucity of information on antiretroviral interaction knowledge in the South African setting, where much ART is nurse‐led. The study aimed to determine this knowledge and to describe which variables were associated with gaps in knowledge.MethodsAn anonymous online survey of healthcare workers in the field of HIV was conducted in August/September 2020. The survey was designed, tested and piloted, and included sections on demographics, guideline access and training, interaction knowledge, counselling and the effect of COVID‐19. Dissemination was via e‐mail and social media (convenience sampling). Descriptive and inferential analysis was done using proportions and the 95% confidence interval to determine relationships between independent and dependent variables. Research ethics approval was obtained from the University of Cape Town's Human Research Ethics Committee (HREC Ref: 357/2020).Results and discussionIn total, 1950 survey responses were included in the analysis – 47.1% nurses, 35.8% doctors and 8.9% pharmacists. When asked whether they were aware that dolutegravir has interactions, 70% said yes, 13.9% said no and 16.1% did not answer. Knowledge of specific interactions and the dosing changes needed was low with a wide range between different drugs: 79.7% knew to double the dolutegravir dose with rifampicin, but with calcium, 5.1% picked both correct dosing options and 33.7% picked one of the two correct options. Access to guidelines and training were positively associated with drug interaction knowledge.ConclusionsThere are gaps in the awareness and knowledge of dolutegravir interactions and how to adjust dosing among South African healthcare workers.

Assessment of Knowledge, Attitude, and Practice of Pharmacists towards Drug Interactions in Saudi Arabia

Background: Drug interactions, which are generally encountered in medical prescriptions, may lead to severe health issues. Pharmacists in both the public and private pharmacy setting are by profession in a unique position to gain and use their competencies to find and prohibit drug interactions.
 Aim of the Study: To assess knowledge, attitude and practice of pharmacists toward drug interactions in Saudi Arabia.
 Materials and Methods: A cross-sectional study was conducted using a self- administered questionnaire.
 Results: Two hundred sixty-three pharmacists participated in the survey and results were expressed in frequencies and percentages. The mean age of participant pharmacists was (31.7) years showing a relatively young participant’s population. Males were (75.3%), while females were (24.7%). Majority of pharmacists (67.3%) were working in public sector. Half of them had a bachelor’s degree in pharmacy as their latest academic degree.
 Participants’ averages of awareness, attitude and practice regarding drug interactions, correct knowledge of drug-drug, drug-food, drug-disease interactions were, (90.7%), (65.3%), (67.7%), (61.7%) respectively. Most (79.8%) of the participants asserted that the notified doctors usually agree with pharmacists’ opinion and decision. This percentage indicates a good cooperation when compared to other studies.
 Conclusion: Pharmacists’ knowledge about drug interactions was inadequate. Lack of knowledge of drug interactions may lead to improper patient counseling and the appearance of adverse effects. There is a need to improve the knowledge and to uplift the level of awareness of pharmacists about the potential drug interactions that are clinically related.

Which antidepressant agent can be used in patients receiving tamoxifen?

IntroductionDepression is frequently observed in carcinology. Many patients with breast cancer, receiving Tamoxifen, may need antidepressants to treat depression. Tamoxifen is a synthetic non-steroidal antioestrogen metabolized by the cytochrome P450 2D6(CYP2D6) to endoxifen which is the active metabolite of this drug. It was reported that the concomitant prescription of Tamoxifen and some antidepressant agents such as paroxetine and fluoxetine may decrease the anticancer effect of tamoxifen as they may inhibit the CYP2D6 pathway.ObjectivesThe objective of this case was to highlight the particularity of management of depression in patients under tamoxifen.MethodsCase report description of a patient treated with Tamoxifen for her breast cancer and who was admitted for major depression, followed by a literature review.ResultsA 36 –year- old woman, had breast cancer and she underwent a mastectomy followed by chemotherapy. Since September 2016, she received 20 mg per day of Tamoxifen as an antihormonal treatment. In November 2018, she was referred to our psychiatry department for depressive symptoms. The patient was sad, she reported social withdrawal, insomnia, anhedonia, and low self-esteem. She had no history of mania or hypomania. A major depressive episode was diagnosed. We prescribed Escitalopram 10 mg per day with clinical improvement. The psychiatric and oncologic status of the patient was stable after two years under tamoxifen and Escitalopram.ConclusionsThe choice of the adequate antidepressant agent in patients under Tamoxifen remains a challenge and requires a thorough knowledge of drug interactions.

Molecular interaction studies of antidepressant drug with aqueous caffeine using volumetric and acoustic methods

Knowledge of drug interactions and the factors controlling these interactions could be useful to overcome their adverse effects on human health. In this study, the effect of caffeine on the antidepressant action of escitalopram oxalate was investigated to explore the behavior of escitalopram oxalate in the presence of caffeine in terms of molecular interactions in an aqueous system at temperature range 293.15 to 313.15 K and atmospheric pressure. Density (ρ) and ultrasonic velocity (u) of escitalopram oxalate in different concentrations of caffeine solutions were measured at the selected range of temperatures. Experimental data has been used to calculate apparent molar volume (Φv) and apparent molar isentropic compressibility (Φv) and fitted to the Redlich–Mayer equation to obtain limiting parameters. Negative deviations from Debye–Huckel limiting law of apparent molar volume for the drug in the presence of caffeine were observed which can be used as a direct measure of the ion-ion and ion–solvent interactions. Partial molar volumes of transfer (ΔtΦvο) were found to be negative for the studied mixture. The partial molar expansibilities and Hepler's constant (∂2∅V0/∂T2) were found to be positive for the mixture (escitalopram oxalate+ caffeine) in water. Intermolecular free length (Lf) and compressibility hydration number (nH) were also analyzed. Obtained results were explored in terms of molecular interactions prevailing among escitalopram oxalate and caffeine molecules in aqueous solutions.

Abstract 4394: Utilizing pharmacokinetics and toxicity data in the translational drug interaction knowledgebase to bridge the phase I cancer drug combination studies

Abstract Multi-target drug combinations have become essential in overcoming de novo and acquired resistance to cancer therapy. However, pharmacokinetics (PK) and toxicity profiles are the largest valley of death for the combinatory drug therapy development. Although there is rich knowledge in the public domain data sources, it is not yet well integrated or readily available to investigators planning novel clinical trial designs. Incorporating the multitude of datasets available for each drug and drug target would allow for more rationale selection of dose levels and schedules in cancer clinical trial development. In this study, we conducted a feasibility study to demonstrate the utility of an integrated drug interaction knowledge base in the designing cancer drug combination phase 1 clinical trials. Methods: Phase 1 or 1b cancer trials reported in ClincalTrials.gov during 1/1/2018-12/31/2018 were used for target trial selection. The multiple interventions in the study were limited to small molecule cancer drugs and monoclonal antibodies. All the identified drugs/chemicals were normalized via DrugBank. Then, our TDIK(Translational Drug Interaction Knowledgebase) was used to assess whether it has sufficient data to infer drug combination PK or toxicity. The TDIK database has integrated PK and toxicity data from more than 1 million DDI evidence in DrugBank, 69,579 articles indexed in the PubMed, drug labels and our Cancer Drug Fraction of Metabolism Database. Results: 423 cancer drug combination phase I/Ib clinical trials were selected as target trials, in which 496 drug combinations were tested, and they include 487 distinct individual drugs. Among those 487 individual drugs, 221 have PK or toxicity data in TDIK. Among 496 drug combinations, 325 combinations have at least PK drug interaction or drug combination toxicity data. Conclusions: TDIK contains a significant amount of PK and toxicity data on cancer drugs and their combinations. It is a powerful tool in designing drug combination phase I trials. Summary of PK and toxicity knowledge identified by TDIKKnowledge Type# of knowledge found in TDIKCommentsIndividual DrugMetabolism or Transport Pathway84Major pathway, the fractions by each pathway, and inhibition capacityMTD (Maximum tolerated dose)154Other PK parameters (Clearance, maximum concentration, half-life time, etc.14This information is for the drugs that are currently in the study or clinical trial phase.Toxicity information (Adverse drug reactions) in phase 1 or 1b trials19This information is for the drugs that are currently in the study or clinical trial phase.Drug CombinationPK or Pharmacodynamics DDI evidence408Toxicity information(Adverse drug reactions) in drug combination trials247All the information is from phase 1 or 1b clinical trials. Citation Format: Lei Wang, Shijun Zhang, Lai Wei, Dwight H. Owen, Lang Li. Utilizing pharmacokinetics and toxicity data in the translational drug interaction knowledgebase to bridge the phase I cancer drug combination studies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4394.

Opinions of primary health care centres' doctors in Bahrain on the electronic software for checking drug interactions in their prescriptions.

An electronic software for checking drug interactions is available at primary health centers in Bahrain. However, it is unclear whether it is optimally utilized by the prescribers. We aimed to explore physicians' attitudes towards the use of the drug interactions checker. This was a cross-sectional study. Data was collected from 18 primary healthcare centres in Bahrain (75%). Primary healthcare physicians responded to a self-administered survey, which was comprised of Likert-type questions. The majority of participants (n=126) were specialist family physicians (91.3%), while the rest were general practitioners (8.7%). Most physicians wrote more than 30 prescriptions in a typical working shift (92.7%); more than half of them contained a combination of drugs (81.5%). Most physicians checked for interactions when prescribing a drug combination (86.9%). Most doctors believed that they had adequate knowledge of drug interactions (73%). However, around one quarter of them were not sure if they would be able to manage them (24%). Although most physicians used the available electronic software (83.9%), many of them relied on their memory to check for interactions (43%). Duration of practice experience, rather than specialization, was correlated with self-reported knowledge (P< 0.001) and ability to manage interactions (P < 0.001). Although most physicians used the software, a significant proportion of them depended on their prior knowledge to identify drug interactions. The importance of optimal use of the software to ensure reliable drug interaction management should be emphasized to the physicians.

Translation of Drug Interaction Knowledge to Actionable Labeling.

Translation of Drug Interaction Knowledge to Actionable Labeling.

Evaluation of community pharmacists’ knowledge about drug–drug interaction in Central Saudi Arabia

IntroductionAlthough all implemented and ongoing initiatives, drug-drug interactions (DDIs) are still a global problem. Most published studies about DDIs in Saudi Arabia are carried out in hospital settings. In addition, assessing the knowledge of drug interactions in Saudi Arabia is limited. The aim of our study is to evaluate the knowledge of potential common drug-drug interactions among community pharmacists particularly in Saudi Arabia. MethodologyA crosses-sectional study utilizing a self- administered questionnaire was conducted among community pharmacy in Riyadh city Saudi Arabia. DDIs' knowledge was assessed by 26 drug pairs. Community pharmacists were asked to select the DDIs as “contraindication”, “may be used together with monitoring”, “no interaction” and “not sure”. ResultsA total of 283 of community pharmacists completed the survey with response rate of 80.9%. Among the 26 drug pairs only 5 of them were identified correctly by most of the participants. To add more 3 out of the 5 pairs had a cutoff of less than 10% between the correct and wrong answer, meaning there still a majority that couldn't identify the correct answer. All the 26 pairs had a statistically significant difference between the correct and incorrect answer. ConclusionThe results of this study showed that knowledge of community pharmacists about DDIs was inadequate. Community pharmacist should have specific courses in drug interactions to cover the most possible interactions that can be seen in this setting.

Modeling polypharmacy side effects with graph convolutional networks.

MotivationThe use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug–drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity.ResultsHere, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein–protein interactions, drug–protein target interactions and the polypharmacy side effects, which are represented as drug–drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug–drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.Availability and implementationSource code and preprocessed datasets are at: http://snap.stanford.edu/decagon.

Knowledge of Drug Interactions to Take Advantage in the Pharmacist

Knowledge of Drug Interactions to Take Advantage in the Pharmacist

Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research.

Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.

A case-history illustrates importance of knowledge of drug-interactions when pain-patients are prescribed non-pain drugs for co-morbidities

A case-history illustrates importance of knowledge of drug-interactions when pain-patients are prescribed non-pain drugs for co-morbidities

Clinical use of nutraceuticals in the adjunctive treatment of depression in mood disorders.

The aim of this paper is to detail a summary of the current evidence in this area, to better inform clinical practice. Our recent systematic reviews and meta-analyses of nutrient pharmacotherapies in the treatment unipolar depression revealed primarily positive results for replicated studies testing S-adenosyl methionine (SAMe), methylfolate, omega-3 (EPA or ethyl-EPA), and Vitamin D; with supportive isolated studies found for creatine and an amino acid combination. Mixed results were found for zinc, folic acid, Vitamin C, and tryptophan; and non-significant study results for inositol. In bipolar depression, omega-3 and N-acetyl cysteine (NAC) were found to have supportive evidence, with an isolated study using a chelated mineral formula also displaying efficacy. No major adverse effects were noted in the studies (aside from occasional minor digestive disturbances with omega-3 and NAC). Several clinical considerations are needed when psychiatrists are considering prescribing nutrients, including knowledge of drug interactions, supplement safety and quality issues, individual psychological and biochemical individualities, in addition to cost factors.

Treatment of Depression in Patients under Breast Cancer Therapy: Antidepressant-Tamoxifen Drug Interactions

The hormone therapy with tamoxifen is used in patients with breast cancer. The tamoxifen mechanism of action is based on the antagonist action of selective estrogen receptors. Depression and anxiety are psychiatric disorders which frequently coexist in patients with breast cancer. In the last decades, there has been an increase in the use of antidepressants. This literature review aims to provide a general view of pharmacokinetic knowledge of drug interactions between tamoxifen and antidepressants. Since tamoxifen is metabolized by CYP2D6, the use of antidepressants that are inhibitors of this is enzyme, such as paroxetine, fluoxetine and bupropion must be avoided. Based on cytochrome p450 drug interactions, it can be concluded that the use of citalopram, escitalopram, desvenlafaxine and venlafaxine are safe and effective treatment unlikely to alter tamoxifen efficacy.