Complexity of the Therapeutic Regimen in Older Adults with Cancer: Associated Factors

BACKGROUND/OBJECTIVES

Population aging is a worldwide phenomenon and is often associated with multimorbidity and polypharmacy. Complex medication regimens are common among older adults and contribute to the occurrence of harmful health outcomes. Age is one of the main risk factors for cancer. This study aimed to determine and characterize the therapeutic complexity in older patients with cancer, and analyze the factors associated with high complexity and the impact of the oncological context.

METHODS

A cross-sectional study with patients aged ≥65 years with cancer was conducted in three hospitals in northern Portugal. Data collection was obtained using self-reports. The medication regimen complexity was assessed using the Medication Regimen Complexity Index (MRCI). Descriptive and association statistical analysis were performed. Logistic, linear, simple and multiple regression analysis were conducted, with and without automatic variable selection.

RESULTS

A total of 552 patients were included (median age, 71; IQR, 68-76). The mean MRCI before the oncological context was 18.67 (SD 12.60) and 27.39 (SD 16.67) after the oncological context, presenting a statistically significant difference in the values obtained (p < 0.001). An elevated complexity was significantly associated with polypharmacy, chronic diseases and with the administration of high-risk medications (p < 0.05). High MRCI values showed a relationship with the occurrence of potential drug interactions (p < 0.001). There was no relationship with the existence of cardiac risk comorbidity.

CONCLUSIONS

This study demonstrated the existence of high therapeutic complexity in older patients with cancer, suggesting the need for intervention to prevent medication-related problems in this vulnerable population.

Polypharmacy and drug interactions in older patients with cancer receiving chemotherapy: associated factors

BACKGROUND

Polypharmacy in older adults with cancer receiving chemotherapy leads to increased risks of drug interactions, translating in potential hazardous health outcomes. This study aims to assess the prevalence of polypharmacy, drug-drug interactions (DDIs), and severe-drug interactions (SDIs) in older patients with cancer. Antineoplastic agents (ANAs) involvement and possible risk contexts (comorbidities with cardiac risk, and high-risk medications) were also analysed.

METHODS

Observational study with older adults (≥ 65 years) diagnosed with cancer, who were treated with antineoplastic agents (ANAs); it was conducted in three hospitals from the north of Portugal. Data collection was obtained using self-reports and medical records. DDIs were identified and classified using Micromedex® software. Descriptive and association analyze statistics were performed. Statistical hypothesis tests with p value less than 0.05 were considered significant. All statistical procedures and analysis were performed with R version 4.1.3.

RESULTS

We enrolled 552 patients. Polypharmacy prevalence was 88.40%; 76.45% and 56.16% of the patients presented with DDIs and SDIs, respectively. SDIs with ANAs were found in 21.20% of the patients. High-risk medications were associated with a higher risk of polypharmacy, DDIs, and SDIs. Polypharmacy and DDIs were higher in patients with hypertension or diabetes. SDIs were higher in patients with diabetes.

CONCLUSION

Polypharmacy, potential DDIs and SDIs were highly prevalent in older adults with cancer. A careful review of the medication administered is necessary to decrease it. These findings warrant further research to optimize medication in this population and decrease problems related to medication, which may lead to emergency room visits and hospitalisations, compromising patient safety and/or ongoing treatments.

Potential drug-drug interaction and its determinants among patients with cancer receiving chemotherapy in oncology centres of Northwest Ethiopia: an institutional-based cross-sectional study

OBJECTIVE

The study was conducted to assess potential drug-drug interactions (PDDIs) and its determinants among patients with cancer receiving chemotherapy.

DESIGN AND SETTING

An institutional-based cross-sectional study was used. This study was conducted from 1 June 2021 to 15 December 2021, in Northwest Ethiopia oncology centres.

PARTICIPANTS

All eligible patients with cancer received a combination of chemotherapy.

OUTCOMES

The prevalence and severity of PDDIs were evaluated using three drug interaction databases. Characteristics of participants were presented, arranged and summarised using descriptive statistics. The predictors and outcome variables were examined using logistic regression. The cut-off point was a p value of 0.05.

RESULTS

Of 422 patients included in the study, 304 patients were exposed to at least one PDDI with a prevalence of 72.1% (95 % CI: 68% to 76%) using three drug interaction databases. There were varied reports of the severity of PDDI among databases, but the test agreement using the kappa index was 0.57 (95% CI: 0.52 to 0.62, p=0.0001) which is interpreted as a moderate agreement among three databases. Patients aged ≥50 years old had the risk to be exposed to PDDI by odds of 3.1 times (adjusted OR (AOR)=3.1, 95% CI (1.8 to 5.3); p=0.001) as compared with patients <50 years old. Similarly, patients with polypharmacy and comorbidity were more likely to be exposed to PDDI than their counterparts (AOR=2.4, 95% CI (1.4 to 4.1); p=0.002 and AOR=1.9, 95% CI (1.1 to 3.4); p=0.02, respectively).

CONCLUSION

The main finding of this study is the high prevalence of PDDI, signifying the need for strict patient monitoring for PDDIs among patients with cancer receiving chemotherapy. We suggest the use of at least three drug databases for quality screening. Patients with an age ≥50 years old, polypharmacy and comorbidity were significantly associated with PDDIs. The establishment of oncology clinical pharmacists and computerised reminder mechanisms for PDDIs through drug utilisation review is suggested.

[Contribution of the pharmaceutical record in the analysis of drug interactions during retrocession in a centre for cancer research]

OBJECTIVES

To implement the pharmaceutical record in retrocession, to evaluate its contribution to the analysis of drug interactions and to estimate the sustainability of this approach.

METHODS

This prospective, descriptive, monocentric study was conducted over five months. All patients presenting at the retrocession were eligible. After having offered them the pharmaceutical record and having completed it, drug interactions were sought. If the impact was considered significant, a pharmaceutical intervention was transmitted to the referring physician of the institution and recorded in the computerized patient record.

RESULTS

The pharmaceutical record was offered to 497 patients, i.e., 87 % of eligible patients. At the first meeting, 7 % of patients (n=34) were aware of it and 72 % had one open. In total, 395 pharmaceutical records were filled in at least once, 41 of which we created. Only 25 patients (5 %) refused the process and 90 % of the existing records were filled by the pharmacy. In total, 419 prescriptions were analysed for 330 patients: the pharmaceutical record was therefore a useful tool for 66 % of patients. For 17 % (n=57) of them, or 11 % of included patients, 99 drug interactions with a high risk of clinical impact were detected with the retroceded drug. On average, the presentation, creation and feeding of the drug record took one minute each and the analysis of interactions 14minutes.

CONCLUSIONS

Easy to implement, the pharmaceutical record is a useful tool to search for drug interactions with retroceded drugs. It helps to optimize patient follow-up, despite the limited information available.

Mitigating the Risk of Drug Interactions in Cancer Patients Taking Oral Anticancer Agents: The Role of a Multidisciplinary Team-Based Medication Reconciliation

PURPOSE

Polypharmacy in cancer patients is a recognized issue and should be an integral part of comprehensive patient assessment and management. Despite this, a systematic review of concomitant drugs or a search for potential drug-drug interactions (DDIs) is not always performed. Here, we present the results of a medication reconciliation model performed by a multidisciplinary team to identify clinically meaningful potential DDIs (defined by the presence of DDI of major severity or contraindication) in cancer patients undergoing oral antineoplastic drugs.

METHODS

From June to December 2022, we performed a non-interventional, prospective, cross-sectional, single-center study of adult cancer patients, initiating or undergoing treatment with oral antineoplastic drugs, referred by their oncologists for therapeutic review regarding potential DDIs. DDIs were assessed by a multidisciplinary team of hospital pharmacists and medical oncologists, through research in three different drug databases as well as in the summary of product characteristics. A report detailing all potential DDIs was created for each request and provided to the patient's medical oncologist for further examination.

RESULTS

Overall, 142 patients' medications were reviewed. Regardless of the severity or clinical importance, 70.4% of patients had at least one potential DDI. We found 184 combinations of oral anticancer and regular therapy agents with potential DDIs, 55 of whom were considered of major severity by at least one DDI database. As expected, the number of potential DDIs increased with the number of active substances in regular therapy (p < 0.001), but we did not find an increased relation between age and the total number of potential DDIs (p = 0.109). Thirty-nine (27.5%) patients had at least one clinically meaningful DDI identified. After adjustment through multivariable logistic regression, only the female sex (odds ratio (OR) 3.01, p = 0.029), the number of active comorbidities (OR 0.60, p = 0.029), and the presence of proton pump inhibitors in chronic medication (OR 2.99, p = 0.033) remained as predictors of potential meaningful DDI.

CONCLUSION

Although drug interactions are a concern in oncology, a systematic DDI review is rarely conducted in medical oncology consultations. The availability of a medication reconciliation service, carried out by a multidisciplinary team with dedicated time for this task, is an added value for safety enhancement in cancer patients.

Antiretroviral drug-drug interactions: A comparison of online drug interaction databases

WHAT IS KNOWN AND OBJECTIVE

Antiretrovirals have a high drug interaction potential, which can lead to increased toxicity and/or decreased efficacy. Multiple databases are available to assess drug-drug interactions. The aim of our study was to compare interaction identification for commonly used ARVs and concomitant medications between six different online drug-drug interaction databases.

COMMENT

This was a cross-sectional review using each of the following six databases: LexiComp®, Clinical Pharmacology®, Micromedex®, Epocrates®, University of Liverpool, and University of Toronto. Sixteen antiretroviral drugs and 100 of the DrugStats Database "Top 200 of 2019" list of medications were included. Each of the six databases identified a different number of actual or potential interactions. The number of interactions ranged from 211 to 283.

WHAT IS NEW AND CONCLUSIONS

A variety of databases exist with inconsistent identification of actual or potential drug-drug interactions amongst them. It may be beneficial to cross-reference multiple databases prior to making decisions regarding patient care.

A comparison of four drug-drug interaction databases for patients undergoing haematopoietic stem cell transplantation

WHAT IS KNOWN AND OBJECTIVE

Patients who have undergone haematopoietic stem cell transplantation are prone to drug-drug interactions due to polypharmacy. Drug-drug interaction databases are essential tools for identifying interactions in this patient group. However, drug-drug interaction checkers, which help manage interactions, may have disagreements about assessing the existence or severance of the interactions. The study aimed to determine differences among popular drug-drug interaction databases from several angles for patients who underwent haematopoietic stem cell transplantation.

METHODS

The 21-day treatment sheets of one hundred patients who underwent haematopoietic stem cell transplantation were examined in two subscription-based (Uptodate and Micromedex) and two open-access databases (Drugs.com and Epocrates) in terms of several categories two years in a row. Statistical analysis was utilized to understand the compatibility of databases in terms of severity scores, evidence levels, given references, and word counts in interaction reports. Fleiss' and Cohen's kappa statistics were used to analyse the databases' agreement levels.

RESULTS AND DISCUSSION

A total of 1393 and 1382 different drug-drug interactions were detected in subsequent versions of the databases, namely the 2021 and 2022 versions. The Fleiss kappa overall agreement among databases was slight. Uptodate and Micromedex showed fair agreement, and other database pairs showed slight agreement in severity ratings.

CONCLUSION

There was a poor agreement among databases for interactions seen in bone marrow transplantation patients. Therefore, it would be safer to use more than one database in daily practice. Further work needs to be done to understand the agreement level of databases for different types of interactions.

Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making

Potential drug-drug interactions (DDIs) are a core concern across medical decision support systems. Among healthcare practitioners, the common practice for screening these interactions is via computer software. However, as real-world negative reporting is missing, counterexamples that serve as contradictory evidence may exist. In this study, we have developed an anti-DDI resource, a set of drug combinations having negative reported interactions. This resource was created from a set of the top 200 most-used drugs, resulting in 14365 prospective negative reported DDI pairs. During analysis and filtering, 2110 DDIs (14.69%) were found in publicly free DDI resources, another 11130 (77.48%) were filtered by a rule-based inference engine incorporating ten mechanisms of interaction, and 208 were identified through commercial resources. Additionally, 90 pairs were removed due to recent FDA approvals or being unapplicable in clinical use. The final set of 827 drug pairs represents combinations potentially having negative reported interactions. The anti-DDI resource is intended to provide a distinctly different direction from the state of the art and establish a ground focus more centered on the evaluation and utilization of existing knowledge for performing thorough assessments. Our negative reported DDIs resource shall provide healthcare practitioners with a level of certainty on DDIs that is worth investigating.

Treatment Algorithm in Cancer-Associated Thrombosis: Updated Canadian Expert Consensus

Patients with cancer-associated thrombosis (CAT) are at high risk of recurrent venous thromboembolism (VTE) and major bleeding complications. Risks vary significantly between individuals based on cancer status, treatment, and other characteristics. To facilitate the evidence-based management of anticoagulant therapy in this patient population, a committee of 11 Canadian clinical experts updated a consensus-based algorithm for the acute and extended treatment of symptomatic and incidental CAT that was developed in 2018. Following a systematic review of the literature, updates to the algorithm were discussed during an online teleconference, and the algorithm was subsequently refined based on feedback from committee members. Clinicians using this treatment algorithm should consider bleeding risk, type of cancer, and drug-drug interactions, as well as patient and clinician preferences, in tailoring anticoagulation for patients with CAT. Anticoagulant therapy should be adapted as the patient's cancer status and management change over time.

Assessment of Drug Interactions with Online Electronic Checkers in Multi-Pathological Patients

INTRODUCTION

Multi-pathological patients are at high risk of drug interactions and side effects. We aimed to assess the usefulness of 3 online drug interaction checkers.

METHODS

In a cross-sectional study, carried out in the Medicine Department of Hospital General of Castellon, Spain, in February 2020, we assessed drug interaction detection with 3 online electronic checkers, Drugs.com, Lexicomp®, and Medscape, and compared results obtained with the 3 tools. From every hospitalized patient, we obtained the list of drugs he or she had been taking until admission. Bivariable tests were used for analysis. p values <0.05 were considered significant.

RESULTS

We included data from 134 patients; 68 (51%) were male; median (and interquartile range) of their age was 82 (76-88) years. A total of 1,082 substance drugs were entered in the checkers. The number of highest grade interactions found with every program was Drugs.com 85, Lexicomp® 33, and Medscape 67. Positive correlations were found between age and number of drug substances prescribed (p = 0.009) and between number of drug substances prescribed and interactions found with any of the 3 checkers (p < 0.001 in all 3 cases). Regarding highest grade interactions, agreement among all 3 checkers was poor.

CONCLUSIONS

The 3 online checkers we assessed found a large number of interactions. The 3 programs gave very discrepant results. Impact on Practice Statements: The analyzed programs, Drugs.com, Lexicomp®, and Medscape Interactions, found a large number of drug interactions in the studied patients. The 3 programs gave very discrepant results among them.

Real-life drug-drug and herb-drug interactions in outpatients taking oral anticancer drugs: comparison with databases

PURPOSE

Due to polypharmacy and the rising popularity of complementary and alternative medicines (CAM), oncology patients are particularly at risk of drug-drug interactions (DDI) or herb-drug interactions (HDI). The aims of this study were to assess DDI and HDI in outpatients taking oral anticancer drug.

METHOD

All prescribed and non-prescribed medications, including CAM, were prospectively collected by hospital pharmacists during a structured interview with the patient. DDI and HDI were analyzed using four interaction software programs: Thériaque^®, Drugs.com^®, Hédrine, and Memorial Sloan Kettering Cancer Center (MSKCC) database. All detected interactions were characterized by severity, risk and action mechanism. The need for pharmaceutical intervention to modify drug use was determined on a case-by-case basis.

RESULTS

294 patients were included, with a mean age of 67 years [55-79]. The median number of chronic drugs per patient was 8 [1-29] and 55% of patients used at least one CAM. At least 1 interaction was found for 267 patients (90.8%): 263 (89.4%) with DDI, 68 (23.1%) with HDI, and 64 (21.7%) with both DDI and HDI. Only 13% of the DDI were found in Thériaque^® and Drugs.com^® databases, and 125 (2.5%) were reported with similar level of risk on both databases. 104 HDI were identified with only 9.5% of the interactions found in both databases. 103 pharmaceutical interventions were performed, involving 61 patients (20.7%).

CONCLUSION

Potentially clinically relevant drug interaction were frequently identified in this study, showing that several databases and structured screening are required to detect more interactions and optimize medication safety.

Sensitivity and specificity of drug interaction databases to detect interactions with recently approved oral antineoplastics

Rationale:

Drug-drug interactions (DDIs) with oral antineoplastics (OAs) are of increasing concern given the rapid increase in OA approvals and use in cancer patients. A small pilot study of 20 DDIs with OAs showed significant variability in commonly used DDI screening databases in sensitivity of detecting potentially clinically relevant DDIs. This study builds upon that work by expanding the number of potential DDIs analyzed and including a specificity analysis.

Methods

Newly approved OAs from 2016 to May 2019 (n = 22) were included in this analysis. Prescribing information for each drug was reviewed. A list of explicit and theoretical drug interactions was created for each OA by the two investigators. A board-certified oncology pharmacist adjudicated all DDI pairs for potential clinical significance. In total, 229 DDI pairs were used to analyze sensitivity of 5 DDI databases (Lexicomp®, Micromedex®, Medscape, Eporactes®, & Drugs.com). Additionally, 64 “dummy” or false DDI pairs were created to analyze specificity. Sensitivity and specific were analyzed using Cochran’s Qtest, while accuracy was analyzed using chi-square test.

Results

There was significant variability among the databases with regards to sensitivity (p < 0.0001), specificity (p < 0.0001), and accuracy (p < 0.0001). In terms of accuracy (max score = 400), Lexicomp®(355), Epocrates® (344), and Drugs.com (352) scored higher than MicroMedex® (270) and Medscape (280).

Conclusions

Considerable variability exists among DDI screening databases with regards to OAs and potential drug interactions. Clinicians should be vigilant in both screening for DDIs with OAs and describing DDIs encountered in clinical practice.

Prevalence and Accuracy of Information on CYP2D6, CYP2C19, and CYP2C9 Related Substrate and Inhibitor Co-Prescriptions in the General Population: A Cross-Sectional Descriptive Study as Part of the PharmLines Initiative

BACKGROUND

Drug-drug interaction (DDI) is one of the main contributors to adverse drug reactions and therefore, it is important to study its frequency in the population. We aimed to investigate frequency and concordance on CYP2D6, CYP2C19, and CYP2C9 (CYP2D6/2C19/2C9)-mediated potential DDIs at the Lifelines cohort and linked data from the pharmacy database IADB.nl.

METHODS

As part of the University of Groningen PharmLines Initiative, data were collected on CYP2D6/2C19/2C9-related substrate/inhibitors from entry questionnaires of Lifelines participants and linked information from the pharmacy database IADB.nl. CYP2D6/2C19/2C9 related co-prescriptions were divided based on the type of drugs i.e. chronically used medication (CM) or occasionally used medication (OM). This resulted in the combination of two chronically used drugs (CM-CM), chronically and occasionally used medication (CM-OM), and two occasionally used drugs (OM-OM). To measure the agreement level, cohen's kappa statistics and test characteristics were used. Results were stratified by time window, gender, and age.

RESULTS

Among 80,837 medicine users in the Lifelines, about 1-2 per hundred participants were exposed to a CYP2D6/2C19/2C9-mediated potential DDI. Overall, the overlapping time window of three months produced the highest mean kappa values between the databases i.e. 0.545 (95% CI:0.544-0.545), 0.512 (95% CI:0.511-0.512), and 0.374 (95% CI:0.373-0.375), respectively. CM-CM had a better level of agreement (good) than CM-OM (fair to moderate) and OM-OM combination (poor to moderate). The influence of gender on concordance values was different for different CYPs. Among older persons, agreement levels were higher than for the younger population.

CONCLUSIONS

CYP2D6/2C19/2C9-mediated potential DDIs were frequent and concordance of data varied by time window, type of combination, sex and age. Subsequent studies should rather use a combination of self-reported and pharmacy database information.

Prevalence and significance of potential drug-drug interactions among cancer patients receiving chemotherapy

Background

Cancer patients often receive multiple drugs to maximize their therapeutic benefit, treat co-morbidities and counter the adverse effects of chemotherapy. Concomitant administration of multiple drugs increases the risk of drug interactions leading to compromised therapeutic efficacy or safety of therapy. The purpose of this study was to identify the prevalence, levels and predictors of potential drug-drug interactions (pDDIs) among cancer patients.

Methods

Six hundred and 78 patients receiving chemotherapy from two tertiary care hospitals were included in this cross-sectional study. Patient medication profiles were screened for pDDIs using the Micromedex® database. Logistic regression analysis was performed to identify the predictors of pDDIs.

Results

The overall prevalence of pDDIs was 78%, majority of patients had 1–2 pDDIs (39.2%). A total of 1843 pDDIs were detected. Major-pDDIs were most frequent (67.3%) whereas, a significant association of pDDIs was found between > 7 all prescribed drugs (p < 0.001) and ≥ 3 anti-cancer drugs (p < 0.001). Potential adverse outcomes of these interactions include reduced therapeutic effectiveness, QT interval prolongation, tendon rupture, bone marrow suppression and neurotoxicity.

Conclusions

Major finding of this study is the high prevalence of pDDIs signifying the need of strict patient monitoring for pDDIs among cancer patients. Patients at higher risk to pDDIs include those prescribed with > 7 any types of drugs or ≥ 3 anticancer drugs. Moreover, list of most frequently identified major and moderate interactions will aid health care professional in timely identification and prevention of pDDIs.

Oral Anticancer Therapy: Management of Drug Interactions

Oral anticancer therapy is increasingly integrated into the care of patients with cancer. Recognition and management of drug-drug interactions (DDIs) is critical to providing efficacious and safe anticancer treatment. DDIs with QTc-prolonging agents, anticoagulants, enzyme inducers and inhibitors, antidepressants, and acid suppressants are commonly encountered with anticancer therapies. Here, we review frequently observed DDIs and outline literature-supported suggestions for their management.

Comparison of eight screening tools to detect interactions between herbal supplements and oncology agents

INTRODUCTION

Patients with cancer are increasingly using herbal supplements, unaware that supplements can interact with oncology treatment. Herb-drug interaction management is critical to ensure optimal treatment outcomes. Several screening tools exist to detect drug-drug interactions, but their performance to detect herb-drug interactions is not known. This study compared the performance of eight drug-drug interaction screening tools to detect herb-drug interaction with anti-cancer agents.

METHODS

The herb-drug interaction detection performance of four subscription (Micromedex, Lexicomp, PEPID, Facts & Comparisons) and free (Drugs.com, Medscape, WebMD, RxList) drug-drug interaction tools was assessed. Clinical relevance of each herb-drug interaction was determined using Natural Medicine and each drug-drug interaction tool. Descriptive statistics were used to calculate sensitivity, specificity, positive predictive value, and negative predictive value. Linear regression was used to compare performance between subscription and free tools.

RESULTS

All tools had poor sensitivity (<0.20) for detecting herb-drug interaction. Lexicomp had the highest positive predictive value (0.98) and best overall performance score (0.54), while Medscape was the best performing free tool (0.52). The worst subscription tools were as good as or better than the best free tools, and as a group subscription tools outperformed free tools on all metrics. Using an average subscription tool would detect one additional herb-drug interaction for every 10 herb-drug interactions screened by a free tool.

CONCLUSION

Lexicomp is the best available tool for screening herb-drug interaction, and Medscape is the best free alternative; however, the sensitivity and performance for detecting herb-drug interaction was far lower than for drug-drug interactions, and overall quite poor. Further research is needed to improve herb-drug interaction screening performance.

Phyotochemical candidates repurposing for cancer therapy and their molecular mechanisms

Though limited success through chemotherapy, radiotherapy and surgery has been obtained for efficient cancer therapy for modern decades, cancers are still considered high burden to human health worldwide to date. Recently repurposing drugs are attractive with lower cost and shorter time compared to classical drug discovery, just as Metformin from Galega officinalis, originally approved for treating Type 2 diabetes by FDA, is globally valued at millions of US dollars for cancer therapy. As most previous reviews focused on FDA approved drugs and synthetic agents, current review discussed the anticancer potential of phytochemicals originally approved for treatment of cardiovascular diseases, diabetes, infectious diarrhea, depression and malaria with their molecular mechanisms and efficacies and suggested future research perspectives.

Nationwide prevalence of potential drug-drug interactions associated with non-anticancer agents in patients on oral anticancer agents in South Korea

PURPOSE

We analyzed the prevalence and severity of potential drug-drug interactions (PDDIs) in Korean patients receiving oral anticancer agents (OAAs) during two different periods.

METHODS

A cross-sectional study was conducted using the national insurance reimbursement database. The subjects were adult outpatients diagnosed with cancer and prescribed OAAs at least once in 2010 or 2014. PDDIs were identified using a database and the PDDI severity was categorized as category X (contraindications) or D (consideration of therapy modification). The associated factors for the occurrence of PDDIs were also analyzed.

RESULTS

Among the 118,258 patients prescribed OAAs in 2014, approximately 59% were middle-aged, and approximately half were diagnosed with breast cancer. The number of comorbidities increased over time, and majority were diagnosed with gastrointestinal disorders, hyperlipidemia, and psychonervous disorders. The PDDIs due to protein kinase inhibitors (PKIs) with gastrointestinal/metabolic and neurological drugs increased 3.1- and 4.9-fold, respectively, over the 5 years, and 24.0% of the PDDIs fell into category X. Tamoxifen, the most commonly prescribed OAAs, caused the PDDIs with antidepressants through QTc prolongation or pharmacokinetic interaction. The PKIs prescription, cancer type like breast or hematologic cancer, and number of comorbidities or co-prescribing drugs were independently associated with the occurrence of PDDIs.

CONCLUSIONS

The risk of PDDIs in patients receiving OAAs increases, particularly with the concomitant use of PKIs with gastrointestinal or psychiatric drugs and endocrine agents with antidepressants. Considering the potential risk of chronic concomitant use of these drug classes in outpatients, healthcare professionals should be made aware of the potential interactions.

Comparison of potential drug-drug interactions with metabolic syndrome medications detected by two databases

Background

Drug-drug interactions (DDIs) are one of the most common drug-related problems. Recently, electronic databases have drug interaction tools to search for potential DDIs, for example, Micromedex and Drugs.com. However, Micromedex and Drugs.com have different abilities in detecting potential DDIs, and this might cause misinformation to occur between patients and health care providers.

Methods and findings

The aim of this study was to compare the ability of Micromedex and Drugs.com to detect potential DDIs with metabolic syndrome medications using the drug list from the U-central database, King Chulalongkorn Memorial Hospital in April 2019. There were 90 available drugs for the treatment of the metabolic syndrome and its associated complications, but six were not found in the Micromedex and Drugs.com databases; therefore, only 84 items were used in the present study. There were 1,285 potential DDI pairs found by the two databases. Micromedex reported DDIs of 724 pairs, while, Drugs.com reported 1,122 pairs. For the severity of the potential DDI reports, the same severity occurred between the two databases of 481 pairs (37.43%) and a different severity for 804 pairs (62.57%).

Conclusion

Drugs.com had a higher sensitivity to detect potential DDIs by approximately 1.5-fold, but Micromedex supplied more informative documentation for the severity classification. Therefore, pharmacists should use at least two databases to evaluate potential DDIs and determine the appropriate drug regimens for physician communications and patient consultations.

Drug databases and their contributions to drug repurposing

Drug repurposing is an interesting field in the drug discovery scope because of reducing time and cost. It is also considered as an appropriate method for finding medications for orphan and rare diseases. Hence, many researchers have proposed novel methods based on databases which contain different information. Thus, a suitable organization of data which facilitates the repurposing applications and provides a tool or a web service can be beneficial. In this review, we categorize drug databases and discuss their advantages and disadvantages. Surprisingly, to the best of our knowledge, the importance and potential of databases in drug repurposing are yet to be emphasized. Indeed, the available databases can be divided into several groups based on data content, and different classes can be applied to find a new application of the existing drugs. Furthermore, we propose some suggestions for making databases more effective and popular in this field.

Comparison of Nine Tools for Screening Drug-Drug Interactions of Oral Oncolytics

PURPOSE

Patients with cancer are an especially vulnerable population to potential drug-drug interactions (DDIs). This makes it important to adequately screen them for DDIs. The objective of this study was to compare the abilities of nine DDI screening tools to detect clinically relevant interactions with oral oncolytics.

METHODS

Subscription-based tools (ie, PEPID, Micromedex, Lexicomp, Facts & Comparisons) and free tools (ie, Epocrates Free, Medscape, Drugs.com, RxList, WebMD) were compared for their abilities to detect clinically relevant DDIs for 145 drug pairs including an oral oncology agent. Clinical relevance was determined by a pharmacist using Stockley's Drug Interactions. Descriptive statistics were calculated for each tool, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), and then compared grouped by free or subscription-based tools for the secondary analysis and analyzed via generalized estimating equations.

RESULTS

For individual metrics, PPV had overall higher values (0.88 to 0.97) relative to the low values included for sensitivity (0.65 to 0.96), specificity (0.53 to 0.93) and NPV (0.38 to 0.83). The top-performing subscription and free tools, Lexicomp and Drugs.com, had no statistically significant differences in performance. Overall, subscription tools had a significantly higher sensitivity (0.85 ± 0.017 v 0.78 ± 0.017; P = .0082) and NPV (0.57 ± 0.039 v 0.48 ± 0.032; P = .031) than free tools. No differences were observed between the specificity and PPV.

CONCLUSION

Due to the low performance of some tools for sensitivity, specificity, and NPV, individual performance should be examined and prioritized on the basis of the intended use when selecting a DDI tool. If a strong-performing subscription-based tool is unavailable, a strong-performing free option, like Drugs.com, is available.

The prevalence of major drug-drug interactions in older adults with cancer and the role of clinical decision support software

OBJECTIVES

Drug-drug interactions (DDIs) represent an escalating concern for older adults attributed to polypharmacy, multi-morbidity and organ dysfunction. Few studies have evaluated the prevalence of major DDIs and the variability between DDI detection software which confuses management.

MATERIALS AND METHODS

Prevalence of major DDIs was examined as a secondary analysis of outpatients aged ≥65 years. Demographic and clinical information was collected from electronic health records including age, sex, race, cancer type, comorbidities, and medications. All DDIs were screened by a clinical pharmacist using Lexi-Interact® and Micromedex®. Major DDIs were defined as Lexi-Interact® category D or X and/or Micromedex® category major or contraindication. Summary statistics of patient characteristics and DDIs were computed.

RESULTS

Our cohort included 142 patients (mean age, 77.7 years; 56% women, 73% Caucasian). The mean medications was 9.8 including 6.7 prescriptions, 2.6 non-prescriptions, and 0.5 herbals. Lexi-Interact® identified 310 major DDIs in 69% of patients (n = 98) with an average of 2.2 DDIs per patient. Micromedex® identified 315 major DDIs in 61% of patients (n = 87) with an average of 2.2 DDIs per patient. DDIs mostly involved opioids, antiplatelets, electrolyte supplements, antiemetics, and antidepressants. Variability existed with the severity rating reporting of the clinical decision support software.

CONCLUSIONS

There was a high prevalence of major DDIs in older adults with cancer. Utilizing clinical decision support software was beneficial for detecting DDIs however, variability existed with severity reporting. Future studies need to identify the relevant DDIs with clinical implications in order to optimize medication safety in this population.

Potential Drug-Drug and Herb-Drug Interactions in Patients With Cancer: A Prospective Study of Medication Surveillance

Purpose:

Patients with cancer frequently use herbal supplements and concomitant medications along with antineoplastic agents. These patients are at high risk of herb-drug interactions (HDIs) and drug-drug interactions (DDIs). We aimed to determine clinically relevant DDIs and HDIs leading to pharmaceutical intervention.

Methods:

Patients starting a new anticancer therapy were asked to complete a questionnaire to identify concomitant use of any over-the-counter drug or herbal supplement. Potential DDIs and HDIs were identified using two different databases. If a potentially clinically relevant DDI was recognized by the clinical pharmacist, a notification was sent to the prescribing oncologist, who decided whether to carry out a suggested intervention. Regression analyses were performed to identify variables associated with clinically relevant DDIs.

Results:

A total of 149 patients were included in this study, with 36 potentially clinically relevant DDIs identified in 26 patients (17.4%; 95% CI, 11.3% to 23.5%), all of them leading to therapy modifications. In total, four patients (2.7%; 95% CI, 0.1% to 5.3%) had experienced clinical consequences from DDIs at the time of pharmacist notification. Additionally, 84 patients (56.4%; 95% CI, 48.4% to 64.4%) reported using concurrent herbal supplements, and 122 possible HDIs were detected. Concomitant use of two or more drugs was independently associated with high risk of a clinically significant DDI (odds ratio, 2.53; 95% CI, 1.08 to 5.91; P = .03).

Conclusion:

Potentially clinically relevant DDIs and possible HDIs were frequently detected in this prospective study. A multidisciplinary approach is required to identify and avoid potentially harmful combinations with anticancer therapy.