Drug interactions in oncology

Drug-Drug interactions prediction calculations between cardiovascular drugs and antidepressants for discovering the potential co-medication risks

Predicting Drug-Drug Interactions (DDIs) enables cost reduction and time savings in the drug discovery process, while effectively screening and optimizing drugs. The intensification of societal aging and the increase in life stress have led to a growing number of patients suffering from both heart disease and depression. These patients often need to use cardiovascular drugs and antidepressants for polypharmacy, but potential DDIs may compromise treatment effectiveness and patient safety. To predict interactions between drugs used to treat these two diseases, we propose a method named Multi-Drug Features Learning with Drug Relation Regularization (MDFLDRR). First, we map feature vectors representing drugs in different feature spaces to the same. Second, we propose drug relation regularization to determine drug pair relationships in the interaction space. Experimental results demonstrate that MDFLDRR can be effectively applied to two DDI prediction goals: predicting unobserved interactions among drugs within the drug network and predicting interactions between drugs inside and outside the network. Publicly available evidence confirms that MDFLDRR can accurately identify DDIs between cardiovascular drugs and antidepressants. Lastly, by utilizing drug structure calculations, we ascertained the severity of newly discovered DDIs to mine the potential co-medication risks and aid in the smart management of pharmaceuticals.

Patients with dementia: prevalence and type of drug-drug interactions

Background

Patients with Alzheimer's disease (AD) and other dementias are more frequently exposed to polymedication, mainly due to the presence of comorbidities, are particularly vulnerable to drug-related problems, and present greater risk of adverse effects due to drug-drug interactions (DDIs).

Purpose

To assess the prevalence of clinically relevant interactions in dementia patients using a routine database, we describe the most frequent interactions and risk factors associated with them to facilitate specific interventions and programs to prevent and minimize them.

Methods

An observational, descriptive, and cross-sectional study that included patients with AD and other types of dementia (n = 100, 64% female) was conducted to identify potential DDI in their treatment using the Lexi-Interact/Lexicomp® database.

Results

A total of 769 drugs were prescribed, involving 190 different active ingredients; 83% of the treatments included five or more drugs. DDI occurred in 87% of the patients, of which 63.2% were female. A total of 689 DDIs were found, grouped in 448 drug pairs, with a mean of 6.9 ± 7.1 (range, 0-31) DDIs per patient, and 680 DDIs were considered clinically relevant. It was observed that 89.8% of the DDIs had a moderate level of severity, 23.5% had a good level of relevance, and pharmacodynamic-based DDIs accounted for 89.5%. The drugs most frequently involved in DDIs were quetiapine (24.5%) and acetylsalicylic acid (10%). A total of 97 DDIs were detected between the acetylcholinesterase inhibitors (AChEIs), and the remaining drugs were administered concomitantly. One of the most frequent DDIs was between AChEIs and beta-blocking agents (n = 29, 4.3%). The most important factors that showed the strongest association with the presence of drug interactions were the use of AChEIs (p = 0.01) and the total number of drugs (p = 0.014) taken by the patient.

Conclusion

Patients with dementia present increased risk of DDIs. Among the most common drugs are psychotropic drugs, which are involved in pharmacodynamic interactions caused by the concomitant use of CNS-targeted drugs. The results highlight the difficulty to evaluate DDIs in clinical practice due to polymedication and variety of comorbidities. Therefore, it is important to review their treatment and consider metabolism inhibition or induction, and potentially P450 substrate overlapping.

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.

A Multicenter Retrospective Observational Study Analyzing the Effect of Polypharmacy on Oxycodone Tolerability

Polypharmacy is becoming increasingly troublesome in the treatment of cancer. The aim of this study was to explore the effects of concomitant polypharmacy comprising drugs that inhibit CYP3A4 and/or CYP2D6 on the oxycodone tolerability in patients with cancer. We conducted a multicenter retrospective study encompassing 20 hospitals. The data used for the study were obtained during the first 2 wk of oxycodone administration. The incidence of oxycodone discontinuation or dose reductions due to side effects and oxycodone-induced nausea and vomiting (OINV) were compared between patients not treated with either inhibitor and those treated with concomitant CYP3A4 or CYP2D6 inhibitors. The incidence of oxycodone discontinuation or dose reductions in patients treated with ≥3 concomitant CYP2D6 inhibitors (18.2%) tended to be higher than that in patients without this treatment (8.2%; p = 0.09). Moreover, the incidence of OINV in patients treated with 2 concomitant CYP3A4 inhibitors (29.8%) was significantly higher than that in patients without this treatment (15.5%; p = 0.049). Multivariate analysis showed that more than two concomitant CYP3A4 inhibitors and no concomitant use of naldemedine were independent risk factors for OINV. Concomitant polypharmacy involving CYP3A4 inhibitors increases the risk of OINV. Therefore, medications concomitantly used with oxycodone should be optimized.

Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities

Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.

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.

Drug-drug interaction prediction: databases, web servers and computational models

In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.

The Impact of Drug-Drug Interactions on the Toxicity Profile of Combined Treatment with BRAF and MEK Inhibitors in Patients with BRAF-Mutated Metastatic Melanoma

BACKGROUND

BRAF and MEK inhibition is a successful strategy in managing BRAF-mutant melanoma, even if the treatment-related toxicity is substantial. We analyzed the role of drug-drug interactions (DDI) on the toxicity profile of anti-BRAF/anti-MEK therapy.

METHODS

In this multicenter, observational, and retrospective study, DDIs were assessed using Drug-PIN software (V 2/23). The association between the Drug-PIN continuous score or the Drug-PIN traffic light and the occurrence of treatment-related toxicities and oncological outcomes was evaluated.

RESULTS

In total, 177 patients with advanced BRAF-mutated melanoma undergoing BRAF/MEK targeted therapy were included. All grade toxicity was registered in 79% of patients. Cardiovascular toxicities occurred in 31 patients (17.5%). Further, 94 (55.9%) patients had comorbidities requiring specific pharmacological treatments. The median Drug-PIN score significantly increased when the target combination was added to the patient's home therapy (p-value < 0.0001). Cardiovascular toxicity was significantly associated with the Drug-PIN score (p-value = 0.048). The Drug-PIN traffic light (p = 0.00821) and the Drug-PIN score (p = 0.0291) were seen to be significant predictors of cardiotoxicity. Patients with low-grade vs. high-grade interactions showed a better prognosis regarding overall survival (OS) (p = 0.0045) and progression-free survival (PFS) (p = 0.012). The survival analysis of the subgroup of patients with cardiological toxicity demonstrated that patients with low-grade vs. high-grade DDIs had better outcomes in terms of OS (p = 0.0012) and a trend toward significance in PFS (p = 0.068).

CONCLUSIONS

DDIs emerged as a critical issue for the risk of treatment-related cardiovascular toxicity. Our findings support the utility of DDI assessment in melanoma patients treated with BRAF/MEK inhibitors.

Anticancer clinical efficiency and stochastic mechanisms of belinostat

Cancer progression is strongly affected by epigenetic events in addition to genetic modifications. One of the key elements in the epigenetic control of gene expression is histone modification through acetylation, which is regulated by the synergy between histone acetyltransferases (HATs) and histone deacetylases (HDACs). HDACs are thought to offer considerable potential for the development of anticancer medications, particularly when used in conjunction with other anticancer medications and/or radiotherapy. Belinostat (Beleodaq, PXD101) is a pan-HDAC unsaturated hydroxamate inhibitor with a sulfonamide group that has been approved by the U.S. Food and Drug Administration (FDA) for the treatment of refractory or relapsed peripheral T-cell lymphoma (PTCL) and solid malignancies or and other hematological tissues. This drug modifies histones and epigenetic pathways. Because HDAC and HAT imbalance can lead to downregulation of regulatory genes, resulting in tumorigenesis. Inhibition of HDACs by belinostat indirectly promotes anti-cancer therapeutic effect by provoking acetylated histone accumulation, re-establishing normal gene expressions in cancer cells and stimulating other routes such as the immune response, p27 signaling cascades, caspase 3 activation, nuclear protein poly (ADP-ribose) polymerase-1 (PARP-1) degradation, cyclin A (G2/M phase), cyclin E1 (G1/S phase) and other events. In addition, belinostat has already been discovered to increase p21WAF1 in a number of cell lines (melanoma, prostate, breast, lung, colon, and ovary). This cyclin-dependent kinase inhibitor actually has a role in processes that cause cell cycle arrest and apoptosis. Belinostat's clinical effectiveness, comprising Phase I and II studies within the areas of solid and hematological cancers, has been evidenced through several investigative trials that have supported its potential to be a valuable anti-cancer drug. The purpose of this research was to provide insight on the specific molecular processes through which belinostat inhibits HDAC. The ability to investigate new therapeutic options employing targeted therapy and acquire a deeper understanding of cancer cell abnormalities may result from a better understanding of these particular routes.

MCFF-MTDDI: multi-channel feature fusion for multi-typed drug-drug interaction prediction

Adverse drug-drug interactions (DDIs) have become an increasingly serious problem in the medical and health system. Recently, the effective application of deep learning and biomedical knowledge graphs (KGs) have improved the DDI prediction performance of computational models. However, the problems of feature redundancy and KG noise also arise, bringing new challenges for researchers. To overcome these challenges, we proposed a Multi-Channel Feature Fusion model for multi-typed DDI prediction (MCFF-MTDDI). Specifically, we first extracted drug chemical structure features, drug pairs' extra label features, and KG features of drugs. Then, these different features were effectively fused by a multi-channel feature fusion module. Finally, multi-typed DDIs were predicted through the fully connected neural network. To our knowledge, we are the first to integrate the extra label information into KG-based multi-typed DDI prediction; besides, we innovatively proposed a novel KG feature learning method and a State Encoder to obtain target drug pairs' KG-based features which contained more abundant and more key drug-related KG information with less noise; furthermore, a Gated Recurrent Unit-based multi-channel feature fusion module was proposed in an innovative way to yield more comprehensive feature information about drug pairs, effectively alleviating the problem of feature redundancy. We experimented with four datasets in the multi-class and the multi-label prediction tasks to comprehensively evaluate the performance of MCFF-MTDDI for predicting interactions of known-known drugs, known-new drugs and new-new drugs. In addition, we further conducted ablation studies and case studies. All the results fully demonstrated the effectiveness of MCFF-MTDDI.

Improving drug-drug interactions prediction with interpretability via meta-path-based information fusion

Drug-drug interactions (DDIs) are compound effects when patients take two or more drugs at the same time, which may weaken the efficacy of drugs or cause unexpected side effects. Thus, accurately predicting DDIs is of great significance for the drug development and the drug safety surveillance. Although many methods have been proposed for the task, the biological knowledge related to DDIs is not fully utilized and the complex semantics among drug-related biological entities are not effectively captured in existing methods, leading to suboptimal performance. Moreover, the lack of interpretability for the predicted results also limits the wide application of existing methods for DDIs prediction. In this study, we propose a novel framework for predicting DDIs with interpretability. Specifically, we construct a heterogeneous information network (HIN) by explicitly utilizing the biological knowledge related to the procedure of inducing DDIs. To capture the complex semantics in HIN, a meta-path-based information fusion mechanism is proposed to learn high-quality representations of drugs. In addition, an attention mechanism is designed to combine semantic information obtained from meta-paths with different lengths to obtain final representations of drugs for DDIs prediction. Comprehensive experiments are conducted on 2410 approved drugs, and the results of predictive performance comparison show that our proposed framework outperforms selected representative baselines on the task of DDIs prediction. The results of ablation study and cold-start scenario indicate that the meta-path-based information fusion mechanism red is beneficial for capturing the complex semantics among drug-related biological entities. Moreover, the results of case study demonstrate that the designed attention mechanism is able to provide partial interpretability for the predicted DDIs. Therefore, the proposed method will be a feasible solution to the task of predicting DDIs.

Oral Chemotherapeutic Ingestions Reported to a Poison Control Center Treated in a Health Care Facility

BACKGROUND

The approach to cancer chemotherapy has changed in recent years, and there are several new oral chemotherapeutics that offer convenience to patients. These medications have toxicity, which may be particularly amplified in an overdose.

STUDY DESIGN

This was a retrospective review of all oral chemotherapy overdoses reported to the California Poison Control System between January 2009 and December 2019. Inclusion criteria were all ingestions coded as "antineoplastic, monoclonal antibody, or thalidomide" that were evaluated at a health care facility. We evaluated outcomes per AAPCC criteria (stratified as death, major, moderate, mild, or no effect) as well as symptoms and interventions.

RESULTS

There were 314 total cases reported; 169 single-substance ingestions (54%) and 145 cases with coingestant(s) (46%). One hundred eighty cases were female (57%) and 134 male (43%). Age ranges were as follows: ages 1-10 years old (87 cases); ages 11-19 years old (26 cases); 20-59 years old (103 cases); ages 60 and older (98 cases). The majority of cases were unintentional ingestions (199, 63%). The most common medication reported was methotrexate with 140 cases (45%), followed by anastrozole (32 cases) and azathioprine (25 cases). One hundred thirty-eight cases were admitted to the hospital for further care (ICU 63 cases; non-ICU 75 cases). Eighty-four of the methotrexate cases received the antidote leucovorin (60%). Five of the capecitabine ingestions received uridine (36%). Outcomes included 124 cases with no effect, 87 cases with minor effect, 73 case with moderate effect, 26 cases with major effect, and 4 deaths.

CONCLUSION

Although methotrexate is the most common oral chemotherapeutic agent involved in overdoses reported to the California Poison Control System, there are many other oral chemotherapeutics from various drug classes, which can lead to toxicity. Although deaths are rare, further studies are needed to determine if particular drugs or drug classes warrant more scrutiny.

The 5 most frequently asked questions about factor Xa inhibitors

Direct oral anticoagulants (DOACs) are commonly used oral factor Xa inhibitors in recent years. However, in some special clinical situations, the appropriate use of these anticoagulants may be of concern. In this article, we address the 5 commonly asked questions regarding their use for the treatment of venous thromboembolism, including in the setting of obesity, renal impairment, gastrointestinal (GI) malignancy, catheter-related thrombosis, and drug-drug interactions. Data on the use of DOACs in the presence of significant obesity or renal failure are mainly observational. Some DOACs are shown to have an increased risk of bleeding in patients with unresected luminal GI malignancy but not others, so selection of appropriate patients is the key. Furthermore, literature on the use of DOACs for catheter-related thrombosis or when drug-drug interactions are of concern is limited, and more research is welcome.

Cytochrome P-450-mediated herb and food-drug interactions can be identified in cancer patients through patient self-reporting with a tablet application: results of a prospective observational study

BACKGROUND

Consumption of herbs, food used as medicine and dietary supplements (HFDSs) is common in cancer patients. Herbs and food-drug interactions (HFDIs) can lead to serious adverse effects and can be prevented. We previously reviewed cytochrome P-450 (CYP)-mediated HFDI for 261 HFDSs and we classified the risk of CYP inhibition and induction on a level of evidence scale from 1 (high evidence, supported by several clinical studies) to 5 (low evidence, only limited preclinical data).

PATIENTS AND METHODS

We conducted a prospective, non-interventional study (NCT04128865) to assess whether self-assessment of patients could detect HFDI classified as 'probable' (i.e. level 1, 2 or 3 of the scale) in a population of cancer patients. Patients were invited through a tablet application to report their consumption of herbs, regular CYP-interacting food consumption and dietary supplements, as well as some clinical data and cancer treatments. The patient's completion of the survey could be supervised by a health care professional or not. A prespecified threshold of 5% of HFDIs classified as 'probable' detected with the application was deemed relevant.

RESULTS

Between 29 March 2018 and 22 June 2018, 143 patients completed the survey. Ninety-five patients (66%) reported at least one current systemic cancer treatment and were included in the analyses. Seventy-four patients reported an intake of at least one HFDS (77.9%), while 21 patients reported no HFDS (22.1%). Twenty-two HFDIs classified as 'probable' were found in 16 patients (16.8%) with the application, which was significantly superior to the prespecified threshold (P = 0.02). The interactions were reported with food (n = 19, 86%) more frequently than with herbs (n = 3, 14%) or with dietary supplements (no interaction reported).

CONCLUSIONS

Self-assessment of HFDS interaction with cancer treatment with an application is feasible and should be considered in daily routine. Prospective interventional studies should be conducted to better assess the clinical benefits of this approach.

Prevalence of Potential Pharmacological Interactions in Patients Undergoing Systemic Chemotherapy in a Tertiary Hospital

Introduction: Pharmacological interactions are frequently observed in patients with chronic diseases, and their occurrence is proportional to the amount of medication used daily. Patients undergoing chemotherapy treatment commonly have comorbidities, which favor a greater prevalence of polypharmacy, increasing the risk of drug interactions. Therefore, the aim of this study was to estimate the prevalence of drug interactions in patients undergoing intravenous chemotherapy treated at a hospital oncology service in southern Brazil. Methods: This was an observational study with a cross-sectional design that was carried out with the analysis of secondary data obtained through the review of medical records. The population assessed consisted of all cancer patients who received intravenous chemotherapy from October to December 2020. Results: Out of the 297 patients included in the study, 231 (77.8%) had at least 1 potential pharmacological interaction. In total, 1044 drug interactions were found that were classified according to severity, resulting in 18 (1.7%) contraindicated drug-drug interactions (DDI), 699 (67%) severe, 281 (26.9%) moderate, and 46 (4.4%) minor interactions. There was an association between polypharmacy and the prevalence of drug interactions. Conclusion: The results demonstrate that a large percentage of patients undergoing chemotherapy are susceptible to drug interactions. Thus, it is necessary that prescribers consider all drugs used by patients and, when possible, prescribe alternative drugs that have less potential for interaction in order to prevent drug interactions adverse effects and provide a better prognosis for patients.

A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection

The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's urgent to develop computational approaches to detect drug-drug interactions. In this paper, we conduct a comprehensive review of state-of-the-art computational methods falling into three categories: literature-based extraction methods, machine learning-based prediction methods and pharmacovigilance-based data mining methods. Literature-based extraction methods detect DDIs from published literature using natural language processing techniques; machine learning-based prediction methods build prediction models based on the known DDIs in databases and predict novel ones; pharmacovigilance-based data mining methods usually apply statistical techniques on various electronic data to detect drug-drug interaction signals. We first present the taxonomy of drug-drug interaction detection methods and provide the outlines of three categories of methods. Afterwards, we respectively introduce research backgrounds and data sources of three categories, and illustrate their representative approaches as well as evaluation metrics. Finally, we discuss the current challenges of existing methods and highlight potential opportunities for future directions.

Pharmacist Involvement in Cancer Pain Management: A Systematic Review and Meta-Analysis

This review aimed to critically evaluate the impact of pharmacist involvement in managing pain in cancer patients. Databases (including MEDLINE, Embase and CENTRAL) were searched with a broad search strategy for studies involving pharmacists and cancer pain management until February 10, 2021. The quality of studies and evidence were assessed using standardized tools and GRADE, respectively. A random-effects model was used for meta-analysis. Sixty-four studies were included. Common interventions delivered by the pharmacists were medication review, patient education, adverse drug reactions (ADRs) detection and management, pharmacological recommendations (in dosing and pharmacotherapy choice), and pain assessment. A pooled analysis of 3 randomized control trials showed a significant reduction in pain intensity with a standardized mean difference (SMD) of 0.35 [95% confidence intervals (CI): -0.55, -0.16]. Pooled analyses from nonrandomized studies of interventions also showed significant results in reduction of ADRs with an odds ratio of 0.69 (95% CI: 0.61, 0.79) and improvement in quality of life with SMD of 0.80 (95% CI: 0.29, 1.32). Thus, pharmacists significantly improve the clinical outcomes of cancer patients related to pain. This indicates the involvement of pharmacists directly or in collaboration with healthcare professionals in the oncology team is highly beneficial for the patients. PERSPECTIVES: This systematic review presents a comprehensive evaluation of pharmacist involvement in cancer pain management. This shows the importance of direct involvement of the pharmacist or as an important member of the multidisciplinary oncology team.

Potential Drug Interactions Between Psychotropics and Intravenous Chemotherapeutics Used by Patients With Cancer

Introduction: Patients undergoing cancer treatment usually have comorbidities, and psychiatric disorders are commonly seen in these patients. For the treatment of these psychiatric disorders, the use of psychotropic drugs is common, turning these patients susceptible to untoward drug interactions. Therefore, the aim of this study was to estimate the prevalence of clinically relevant drug-drug interactions (DDI) between chemotherapeutic and psychotropic agents in patients with cancer treated at an oncology service in southern Brazil. Methods: An observational epidemiological study with a cross-sectional census-type design was carried out between October and December 2020. The drug-drug interactions were identified through consultation and analysis of the Medscape Drug Interaction Check and Micromedex databases. The interactions were classified as major, when the interaction can be fatal and/or require medical intervention to avoid or minimize serious adverse effects and moderate, when the interaction can exacerbate the patient's condition and/or requires changes in therapy. Results: A total of 74 patients was included in the study among the 194 patients seen in the oncology service during the period studied. A total of 24 (32.4%) DDIs were found, 21 (87.5%) of which were classified as being of major risk and 3 (12.5%) as moderate risk. According to the mechanism of action, 19 (79.1%) were classified as pharmacodynamic interactions and 5 (20.9%) as pharmacokinetic interactions. Conclusion: It was shown that a considerable percentage of patients undergoing intravenous chemotherapy are at risk of pharmacological interaction with psychotropic drugs. Thus, it is essential that the oncologist considers all psychotropic drugs and other drugs used by patients in order to avoid drug-drug interactions.

A comprehensive evaluation of potentially significant drug-drug, drug-herb, and drug-food interactions among cancer patients receiving anticancer drugs

INTRODUCTION

During the cancer treatment path, cancer patients use numerous drugs, including anticancer, supportive, and other prescribed medications, along with herbs and certain products. This puts them at risk of significant drug interactions (DIs). This study describes DIs in cancer patients and their prevalence and predictors.

METHODS

A cross-sectional study design was used to achieve the study objectives. The study was carried out in two centers in the northern West Bank, Palestine. The Lexicomp® Drug Interactions tool (Lexi-Comp, Hudson OH, USA) was applied to check the potential DIs. In addition, the Statistical Package for the Social Sciences (SPSS) was used to show the results and find the associations.

RESULTS

The final analysis included 327 patients. Most of the participants were older than 50 years (61.2%), female (68.5%), and had a solid tumor (74.6%). The total number of potential DIs was 1753, including 1510 drug-drug interactions (DDIs), 24 drug-herb interactions, and 219 drug-food interactions. Importantly, the prevalence of DDIs was 88.1%. In multivariate analysis, the number of potential DDIs significantly decreased with the duration of treatment (p = 0.007), while it increased with the number of comorbidities (p < 0.001) and the number of drugs used (p < 0.001).

CONCLUSIONS

We found a high prevalence of DIs among cancer patients. This required health care providers to develop a comprehensive protocol to monitor and evaluate DIs by improving doctor-pharmacist communication and supporting the role of clinical pharmacists.

Cardio-Oncology Drug Interactions: A Scientific Statement From the American Heart Association

In the cardio-oncology population, drug interactions are of particular importance given the complex pharmacological profile, narrow therapeutic index, and inherent risk of therapies used to manage cardiovascular disease and cancer. Drug interactions may be beneficial or detrimental to the desired therapeutic effect. Clinicians in both cardiology and oncology should be cognizant of these potential drug-drug interactions that may reduce the efficacy or safety of either cardiovascular or cancer therapies. These risks can be mitigated through increased recognition of potential drug-drug interaction, use of alternative medications when possible, and careful monitoring. This scientific statement provides clinicians with an overview of pharmacodynamic and pharmacokinetic drug-drug interactions in patients with cancer exposed to common cardiovascular and cancer medications.

The Prevalence of Polypharmacy and Potential Drug-Drug Interactions among Iranian Cancer Patients: which Patients Were at Risk?

INTRODUCTION

Drug-Drug interactions (DDIs) are one of the considerable consequences of polypharmacy. Due to the influence of polypharmacy and drug interactions on cancer treatment and patients` health outcomes, the present study aimed to determine the prevalence of polypharmacy and potential DDIs among adult cancer patients in Kerman province, southeast of Iran.

METHODS

In this cross-sectional study, 315 cancer patients who referred to Kerman city oncology clinics were assessed in 2018. Data were collected through face-to-face interviews and medical charts were reviewed. Polypharmacy was defined as the use of at least five drugs simultaneously. Potential DDIs were checked using the "Drugs.com" online database. A bivariable and a multivariable logistic regression were used to determine the associated factors of outcome variables, polypharmacy status and potential DDI in SPSS software version 23.

RESULTS

Of 315 cancer patients, 191 patients (60.6%; 95% CI:54.9, 66) used at least five drugs during chemotherapy courses. The prevalence of potential DDIs was 59.6% (n = 140, 95% CI: 53.6-66.0. Experience co-morbid diseases (OR:6.60; 95%CI: 3.82, 11.42; P-value ≤0.0001), and positive metastatic status (OR:2.80; 95%CI: 1.62, 4.82; P-value ≤0.0001) could predict the polypharmacy during chemotherapy courses. Patients who suffered gastrointestinal cancers (OR:5.55; 95%CI: 2.26, 13.62; P-value ≤0.0001) and the number of prescribed or Over The Counter (OTC) drugs (OR:1.29; 95%CI: 1.12, 1.48; P-value <0.0001) predicted the occurrence of potential DDIs among cancer patients.

CONCLUSION

Regarding the high prevalence of polypharmacy and potential drug interactions among Iranian cancer patients during chemotherapy courses, it is advisable for physicians, nurses, and pharmacists to be vigilant to improve prescribing patterns. In addition, with intensive monitoring, alternative treatment strategies can be replaced.

A retrospective evaluation of drug–drug interactions in patients admitted to Internal Medicine Departments in Palestinian Hospitals

Objective:

To measure the prevalence and identify risk factors associated with drug–drug interactions among patients admitted to internal medicine departments in Palestinian hospitals

Methods:

A retrospective cross-sectional observational study was conducted. Data were obtained from patient files from the internal medicine departments in Palestinian hospitals from 1 September 2017, to 31 March 2018. The data collected included patient gender, age, length of hospitalization, medications prescribed, and the number of medications. The digital clinical decision support system IBM Micromedex® was used to assess potential drug–drug interactions.

Results:

The number of patients included in this study is 513. The total number of potential drug–drug interactions detected in study participants is 1558. The average number of potential drug–drug interactions per patient was found to be 3 ± 3.9. Among study participants, 66.1% (n = 339) were found to have potential drug–drug interactions in their current medications. The most commonly encountered drug–drug interactions type was “major” drug–drug interaction, which was encountered in 43.6% (n = 681) of total detected drug–drug interactions. Other types of drug–drug interactions were encountered in 42% (n = 647), 14% (n = 224), and 0.4% (n = 6) which were moderate, minor, and contraindicated drug–drug interactions, respectively. Patients’ age, number of medications, and length of hospitalization were associated with the increased risk of potential drug–drug interactions.

Conclusion:

The results indicated a high prevalence of potential drug–drug interactions in Palestinian hospitals, associated with polypharmacy, increased age, and increased length of hospitalization. Therefore, managing patient medication by a drug expert such as a clinical pharmacist to identify and resolve potential drug–drug interactions will possibly decrease the high prevalence of drug–drug interactions, prevent patient harm, and decrease the cost of hospitalization.

A phase I study of veliparib with cyclophosphamide and veliparib combined with doxorubicin and cyclophosphamide in advanced malignancies

PURPOSE

Veliparib (V), an oral poly(ADP-ribose) polymerase (PARP) inhibitor, potentiates effects of alkylating agents and topoisomerase inhibitors in preclinical tumor models. We conducted a phase I trial of V with iv cyclophosphamide (C) and V plus iv doxorubicin (A) and C.

METHODS

Objectives were to establish the maximum tolerated dose (MTD) of the combinations, characterize V pharmacokinetics (PK) in the presence and absence of C, measure PAR in peripheral blood mononuclear cells (PBMCs) and γH2AX in circulating tumor cells (CTCs). In Group 1, dose escalations of V from 10 to 50 mg every 12 h Days 1-4 plus C 450 to 750 mg/m² Day 3 in 21-day cycles were evaluated. In Group 2, V doses ranged from 50 to 150 mg every 12 h Days 1-4 with AC (60/600 mg/m²) Day 3 in 21-day cycles. In Group 3, patients received AC Day 1 plus V Days 1-7, and in Group 4, AC Day 1 plus V Days 1-14 was given in 21-day cycles to evaluate effects on γH2AX foci.

RESULTS

Eighty patients were enrolled. MTD was not reached for V and C. MTD for V and AC was V 100 mg every 12 h Days 1-4 with AC (60/600 mg/m2) Day 3 every 21 days. V PK appears to be dose-dependent and has no effect on the PK of C. Overall, neutropenia and anemia were the most common adverse events. Objective response in V and AC treated groups was 22% (11/49). Overall clinical benefit rate was 31% (25/80). PAR decreased in PBMCs. Percentage of γH2AX-positive CTCs increased after treatment with V and AC.

CONCLUSION

V and AC can be safely combined. Activity was observed in patients with metastatic breast cancer.

The potential roles of Nrf2/Keap1 signaling in anticancer drug interactions

Nuclear factor (erythroid-derived 2)-related factor 2 (Nrf2), together with its suppressive binding partner Kelch-like ECH-associated protein 1 (Keap1), regulates cellular antioxidant response and drug metabolism. The roles of Nrf2/Keap1 signaling in the pathology of many diseases have been extensively investigated, and small molecules targeting Nrf2/Keap1 signaling have been developed to prevent or treat diseases such as multiple sclerosis, chronic kidney disease and cancer. Notably, Nrf2 plays dual roles in cancer development and treatment. Activation of Nrf2/Keap1 signaling in cancer cells has been reported to promote cancer progression and result in therapy resistance. Since cancer patients are often suffering comorbidities of other chronic diseases, anticancer drugs could be co-administrated with other drugs and herbs. Nrf2/Keap1 signaling modulators, especially activators, are common in drugs, herbs and dietary ingredients, even they are developed for other targets. Therefore, drug-drug or herb-drug interactions due to modulation of Nrf2/Keap1 signaling should be considered in cancer therapies. Here we briefly summarize basic biochemistry and physiology functions of Nrf2/Keap1 signaling, Nrf2/Keap1 signaling modulators that cancer patients could be exposed to, and anticancer drugs that are sensitive to Nrf2/Keap1 signaling, aiming to call attention to the potential drug-drug or herb-drug interactions between anticancer drugs and these Nrf2/Keap1 signaling modulators.

Megestrol acetate is a specific inducer of CYP3A4 mediated by human pregnane X receptor

PURPOSE

Megestrol acetate is a synthetic progestogen used to treat some cancers and cancer-associated cachexia, but its potential interactions with other drugs are not well known. This study aims to determine the regulation of drug metabolizing enzymes by megestrol acetate.

METHODS

Primary human hepatocytes were treated and analyzed by PCR array to identify genes involved in drug metabolism that are impacted by megestrol acetate. P450 3A4 (CYP3A4) reporter gene assay and HPLC analyses of nifedipine metabolites were used to determine CYP3A4 gene expression and activities. Competitive ligand binding assay was used to determine the affinity of megestrol acetate toward human pregnane x receptor (hPXR). Electrophoretic mobility shift assay and mammalian two hybrid assay were used to determine the mechanism of megestrol to activate hPXR.

RESULTS

The levels and activities of CYP3A4 were significantly induced (> 4-folds) by megestrol acetate in human hepatocytes and HepG2 cells. Megestrol treatment induced CYP3A4 through the activation of hPXR, a ligand-activated transcription factor that plays a role in drug metabolism and transport. Other tested nuclear receptors showed no response. The mechanism studies showed that megestrol activated hPXR by binding to the ligand binding domain (LBD) of hPXR and increasing the recruitment of the cofactors such as steroid receptor cofactor (SRC-1).

CONCLUSION

The results suggest that megestrol acetate is a specific inducer of CYP3A4 mediated by hPXR and therefore has the potential to cause drug interactions, especially in the co-administration with drugs that are substrates of CYP3A4.

In Silico Prediction, Molecular Docking and Dynamics Studies of Steroidal Alkaloids of Holarrhena pubescens Wall. ex G. Don to Guanylyl Cyclase C: Implications in Designing of Novel Antidiarrheal Therapeutic Strategies

The binding of heat stable enterotoxin (STa) secreted by enterotoxigenic Escherichia coli (ETEC) to the extracellular domain of guanylyl cyclase c (ECD_GC-C) causes activation of a signaling cascade, which ultimately results in watery diarrhea. We carried out this study with the objective of finding ligands that would interfere with the binding of STa on ECD_GC-C. With this view in mind, we tested the biological activity of a alkaloid rich fraction of Holarrhena pubescens against ETEC under in vitro conditions. Since this fraction showed significant antibacterial activity against ETEC, we decided to test the screen binding affinity of nine compounds of steroidal alkaloid type from Holarrhena pubescens against extracellular domain (ECD) by molecular docking and identified three compounds with significant binding energy. Molecular dynamics simulations were performed for all the three lead compounds to establish the stability of their interaction with the target protein. Pharmacokinetics and toxicity profiling of these leads demonstrated that they possessed good drug-like properties. Furthermore, the ability of these leads to inhibit the binding of STa to ECD was evaluated. This was first done by identifying amino acid residues of ECD_GC-C binding to STa by protein-protein docking. The results were matched with our molecular docking results. We report here that holadysenterine, one of the lead compounds that showed a strong affinity for the amino acid residues on ECD_GC-C, also binds to STa. This suggests that holadysenterine has the potential to inhibit binding of STa on ECD and can be considered for future study, involving its validation through in vitro assays and animal model studies.

Influence of Concomitant Polypharmacy on Docetaxel-induced Febrile Neutropenia

Background/Aim

Docetaxel (DTX) is metabolized by liver cytochromes P450 (CYP) 3A4 (CYP3A4) and 3A5 (CYP3A5) CYP3A4 activity is considered the main factor affecting the effectiveness in DTX clearance. We, therefore, explored the association between DTX-induced febrile neutropenia (FN) and concomitant polypharmacy involving CYP3A4 inhibitors in cancer patients.

Patients and Methods

Among patients who received docetaxel, we compared the number of concomitant medications between patients with and without FN, and risk factors associated with FN were identified.

Results

The total number of concomitant CYP3A4 inhibitors and substrates used was significantly higher in patients with FN [mean: 2.1 (95% confidence interval (CI)=1.5-2.9)] than in those without FN [mean: 1.4 (95% CI=1.0-1.8)] (p=0.01). The only risk factor for FN was the use of ≥2 concomitant CYP3A4 inhibitors and substrates in total (OR=4.82, 95% CI=1.77-14.1; p=0.002).

Conclusion

Polypharmacy involving CYP3A4 inhibitors and substrates increases the risk of DTX-induced FN.

New drugs and pharmacological interactions in real life

A high number of new drugs have entered clinical development and many of them have recently been approved for patients with lymphoid malignancies. The availability of new drugs offers additional treatment options, but it also requires particular attention for the emergence of adverse events. In addition, new drugs may also have interactions with other drugs, which could further increase the risk of toxicities or result in decreased efficacy. Here we review potential drug interactions for nonchemotherapy new drugs approved for patients with lymphoid malignancies.

Napabucasin Drug-Drug Interaction Potential, Safety, Tolerability, and Pharmacokinetics Following Oral Dosing in Healthy Adult Volunteers

Napabucasin is an orally administered reactive oxygen species generator that is bioactivated by the intracellular antioxidant nicotinamide adenine dinucleotide phosphate:quinone oxidoreductase 1. Napabucasin induces cell death in cancer cells, including cancer stem cells. This phase 1 study (NCT03411122) evaluated napabucasin drug‐drug interaction potential for 7 cytochrome P450 (CYP) enzymes and the breast cancer resistance protein transporter/organic anion transporter 3. Healthy volunteers who tolerated napabucasin during period 1 received probe drugs during period 2, and in period 3 received napabucasin (240 mg twice daily; days 1‐11) plus a phenotyping cocktail containing omeprazole (CYP2C19), caffeine (CYP1A2), flurbiprofen (CYP2C9), bupropion (CYP2B6), dextromethorphan (CYP2D6), midazolam (CYP3A) (all oral; day 6), intravenous midazolam (day 7), repaglinide (CYP2C8; day 8), and rosuvastatin (breast cancer resistance protein/organic anion transporter 3; day 9). Drug‐drug interaction potential was evaluated in 17 of 30 enrolled volunteers. Napabucasin coadministration increased the area under the plasma concentration–time curve from time 0 extrapolated to infinity (geometric mean ratio [90% confidence interval]) of caffeine (124% [109.0%‐141.4%]), intravenous midazolam (118% [94.4%‐147.3%]), repaglinide (127% [104.7%‐153.3%]), and rosuvastatin (213% [42.5%‐1068.3%]) and decreased the area under the plasma concentration–time curve from time 0 extrapolated to infinity of dextromethorphan (71% [47.1%‐108.3%]), bupropion (79% [64.6%‐97.0%]), and hydroxybupropion (45% [15.7%‐129.6%]). No serious adverse events/deaths were reported. Generally, napabucasin is not expected to induce/inhibit drug clearance to a clinically meaningful degree.

Potential Cytochrome P450-mediated pharmacokinetic interactions between herbs, food, and dietary supplements and cancer treatments

Herbs, food and dietary supplements (HFDS), can interact significantly with anticancer drug treatments via cytochrome p450 isoforms (CYP) CYP3A4, CYP2D6, CYP1A2, and CYP2C8. The objective of this review was to assess the influence of HFDS compounds on these cytochromes. Interactions with CYP activities were searched for 189 herbs and food products, 72 dietary supplements in Web of Knowledge® databases. Analyses were made from 140 of 3,125 clinical trials and 236 of 3,374 in vitro, animal model studies or case reports. 18 trials were found to report direct interactions between 9 HFDS with 8 anticancer drugs. 21 HFDS were found to interact with CYP3A4, a major metabolic pathway for many anticancer drugs. All 261 HFDS were classified for their interaction with the main cytochromes P450 involved in the metabolism of anticancer drugs. We provided an easy-to-use colour-coded table to easily match potential interactions between 261 HFDS and 117 anticancer drugs.

Influence of serum inflammatory cytokines on cytochrome P450 drug metabolising activity during breast cancer chemotherapy: a patient feasibility study

Individual response to chemotherapy in patients with breast cancer is variable. Obesity and exercise are associated with better and worse outcomes, respectively, and it is known that both impact the systemic cytokine milieu. Cytochrome P450 (CYP) enzymes are responsible for the metabolism of many chemotherapy agents, and CYP enzyme activity has been shown to be modified by inflammatory cytokines in vitro and in vivo. Cytokine-associated changes in CYP metabolism may alter chemotherapy exposure, potentially affecting treatment response and patient survival. Therefore, better understanding of these biological relationships is required. This exploratory single arm open label trial investigated changes in in vivo CYP activity in twelve women treated for stage II or III breast cancer, and demonstrated for the first time the feasibility and safety of utilising the Inje phenotyping cocktail to measure CYP activity in cancer patients receiving chemotherapy. Relative CYP activity varied between participants, particularly for CYP2C9 and CYP2D6, and changes in serum concentrations of the inflammatory cytokine monocyte chemoattractant protein 1 inversely correlated to CYP3A4 activity during chemotherapy. Future use of phenotyping cocktails in a clinical oncology setting may help guide drug dosing and improve chemotherapy outcomes.

Clinical Trial Registration: Trial was retrospectively registered to the Australia New Zealand Clinical Trial Registry (ANZCTR). ACTRN12620000832976, 21 Aug 2020, https://www.anzctr.org.au/ACTRN12620000832976.aspx.

Clinical CYP2D6 Genotyping to Personalize Adjuvant Tamoxifen Treatment in ER-Positive Breast Cancer Patients: Current Status of a Controversy

Simple Summary

Tamoxifen is an important adjuvant endocrine therapy in estrogen receptor (ER)-positive breast cancer patients. It is mainly catalyzed by the enzyme CYP2D6 into the most active metabolite endoxifen. Genetic variation in the CYP2D6 gene influences endoxifen formation and thereby potentially therapy outcome. However, the association between CYP2D6 genotype and clinical outcome on tamoxifen is still under debate, as contradictory outcomes have been published. This review describes the latest insights in both CYP2D6 genotype and endoxifen concentrations, as well CYP2D6 genotype and clinical outcome, from 2018 to 2020.

Abstract

Tamoxifen is a major option for adjuvant endocrine treatment in estrogen receptor (ER) positive breast cancer patients. The conversion of the prodrug tamoxifen into the most active metabolite endoxifen is mainly catalyzed by the enzyme cytochrome P450 2D6 (CYP2D6). Genetic variation in the CYP2D6 gene leads to altered enzyme activity, which influences endoxifen formation and thereby potentially therapy outcome. The association between genetically compromised CYP2D6 activity and low endoxifen plasma concentrations is generally accepted, and it was shown that tamoxifen dose increments in compromised patients resulted in higher endoxifen concentrations. However, the correlation between CYP2D6 genotype and clinical outcome is still under debate. This has led to genotype-based tamoxifen dosing recommendations by the Clinical Pharmacogenetic Implementation Consortium (CPIC) in 2018, whereas in 2019, the European Society of Medical Oncology (ESMO) discouraged the use of CYP2D6 genotyping in clinical practice for tamoxifen therapy. This paper describes the latest developments on CYP2D6 genotyping in relation to endoxifen plasma concentrations and tamoxifen-related clinical outcome. Therefore, we focused on Pharmacogenetic publications from 2018 (CPIC publication) to 2021 in order to shed a light on the current status of this debate.

Drug-related problems and risk factors related to unplanned hospital readmission among cancer patients in Belgium

INTRODUCTION

There are about 60,000 diagnoses of cancer per year in Belgium. After hospital care, about 12-13% of cancer patients are readmitted within 30 days after discharge. These readmissions are partly related to drug-related problems (DRP), such as interactions or adverse drug effects (ADE).

OBJECTIVES

The aim of this study is to quantify and to classify DRP readmissions within 30 days for cancer patients and to highlight risk factors potentially correlated to readmissions.

METHODS

This study is a 6-month observational retrospective study in two care facilities in Brussels: an academic general hospital and an academic oncology center. Patients readmitted within 30 days after their last hospital care for a potential DRP were included. Patient files were evaluated with an intermediate medication review that included interactions analysis (Lexicomp®). The probability of DRP readmission was assessed using the World Health Organization's Uppsala Monitoring Centre (WHO-UMC) system.

RESULTS

The final population included 299 patients; among them, 123 (41.1%) were readmitted due to DRP (certain DRP (4.9%), probable DRP (49.6%), and possible DRP (45.5%)). Risks factors linked to these DRP were a low Charlson Comorbidity Index, polypharmacy, the kind of hospital, and some chemotherapies (platinum preparations). Among all readmitted patients, the D-type interactions were the most common (44.8%), which suggest a possible therapy modification. However, around 10% of interactions were X-type (drug combination to avoid).

CONCLUSION

Almost 10% of patient readmitted within 30 days were potentially related to a DRP, most of them from adverse drug effects. Four risk factors (low Charlson Comorbidity Index, polypharmacy, the hospital, and some chemotherapies) were highlighted to prevent these readmissions.

Multiple Effects of Ascorbic Acid against Chronic Diseases: Updated Evidence from Preclinical and Clinical Studies

Severe disease commonly manifests as a systemic inflammatory process. Inflammation is associated withthe enhanced production of reactive oxygen and nitrogen species and with a marked reduction in the plasma concentrations of protective antioxidant molecules. This imbalance gives rise to oxidative stress, which is greater in patients with more severe conditions such as sepsis, cancer, cardiovascular disease, acute respiratory distress syndrome, and burns. In these patients, oxidative stress can trigger cell, tissue, and organ damage, thus increasing morbidity and mortality. Ascorbic acid (ASC) is a key nutrient thatserves as an antioxidant and a cofactor for numerous enzymatic reactions. However, humans, unlike most mammals, are unable to synthesize it. Consequently, ASC must be obtained through dietary sources, especially fresh fruit and vegetables. The value of administering exogenous micronutrients, to reestablish antioxidant concentrations in patients with severe disease, has been recognized for decades. Despite the suggestion that ASC supplementation may reduce oxidative stress and prevent several chronic conditions, few large, randomized clinical trials have tested it in patients with severe illness. This article reviews the recent literature on the pharmacological profile of ASC and the role of its supplementation in critically ill patients.

Predicting Metabolism-Related Drug-Drug Interactions Using a Microphysiological Multitissue System

Drug-drug interactions (DDIs) occur when the pharmacological activity of one drug is altered by a second drug. As multimorbidity and polypharmacotherapy are becoming more common due to the increasing age of the population, the risk of DDIs is massively increasing. Therefore, in vitro testing methods are needed to capture such multiorgan events. Here, a scalable, gravity-driven microfluidic system featuring 3D microtissues (MTs) that represent different organs for the prediction of drug-drug interactions is used. Human liver microtissues (hLiMTs) are combined with tumor microtissues (TuMTs) and treated with drug combinations that are known to cause DDIs in vivo. The testing system is able to capture and quantify DDIs upon co-administration of the anticancer prodrugs cyclophosphamide or ifosfamide with the antiretroviral drug ritonavir. Dosage of ritonavir inhibits hepatic metabolization of the two prodrugs to different extents and decreases their efficacy in acting on TuMTs. The flexible MT compartment design of the system, the use of polystyrene as chip material, and the assembly of several chips in stackable plates offer the potential to significantly advance preclinical substance testing. The possibility of testing a broad variety of drug combinations to identify possible DDIs will improve the drug development process and increase patient safety.

Predicting adverse drug reactions of two-drug combinations using structural and transcriptomic drug representations to train an artificial neural network

Adverse drug reactions (ADRs) are pharmacological events triggered by drug interactions with various sources of origin including drug-drug interactions. While there are many computational studies that explore models to predict ADRs originating from single drugs, only a few of them explore models that predict ADRs from drug combinations. Further, as far as we know, none of them have developed models using transcriptomic data, specifically the LINCS L1000 drug-induced gene expression data to predict ADRs for drug combinations. In this study, we use the TWOSIDES database as a source of ADRs originating from two-drug combinations. 34,549 common drug pairs between these two databases were used to train an artificial neural network (ANN), to predict 243 ADRs that were induced by at least 10% of the drug pairs. Our model predicts the occurrence of these ADRs with an average accuracy of 82% across a multifold cross-validation.

Physiologically based pharmacokinetic modeling to assess metabolic drug-drug interaction risks and inform the drug label for fedratinib

Purpose

Fedratinib (INREBIC^®), a Janus kinase 2 inhibitor, is approved in the United States to treat patients with myelofibrosis. Fedratinib is not only a substrate of cytochrome P450 (CYP) enzymes, but also exhibits complex auto-inhibition, time-dependent inhibition, or mixed inhibition/induction of CYP enzymes including CYP3A. Therefore, a mechanistic modeling approach was used to characterize pharmacokinetic (PK) properties and assess drug–drug interaction (DDI) potentials for fedratinib under clinical scenarios.

Methods

The physiologically based pharmacokinetic (PBPK) model of fedratinib was constructed in Simcyp^® (V17R1) by integrating available in vitro and in vivo information and was further parameterized and validated by using clinical PK data.

Results

The validated PBPK model was applied to predict DDIs between fedratinib and CYP modulators or substrates. The model simulations indicated that the fedratinib-as-victim DDI extent in terms of geometric mean area under curve (AUC) at steady state is about twofold or 1.2-fold when strong or moderate CYP3A4 inhibitors, respectively, are co-administered with repeated doses of fedratinib. In addition, the PBPK model successfully captured the perpetrator DDI effect of fedratinib on a sensitive CY3A4 substrate midazolam and predicted minor effects of fedratinib on CYP2C8/9 substrates.

Conclusions

The PBPK-DDI model of fedratinib facilitated drug development by identifying DDI potential, optimizing clinical study designs, supporting waivers for clinical studies, and informing drug label claims. Fedratinib dose should be reduced to 200 mg QD when a strong CYP3A4 inhibitor is co-administered and then re-escalated to 400 mg in a stepwise manner as tolerated after the strong CYP3A4 inhibitor is discontinued.

Electronic supplementary material

The online version of this article (10.1007/s00280-020-04131-y) contains supplementary material, which is available to authorized users.

Drug-Drug Interactions of 257 Antineoplastic and Supportive Care Agents With 7 Anticoagulants: A Comprehensive Review of Interactions and Mechanisms

Data on drug–drug interactions (DDI) of antineoplastic drugs with anticoagulants is scarce. We aim to evaluate factors associated with DDI of antineoplastic and supportive care drugs with anticoagulants resulting in modification of pharmacokinetics of these last mentioned. A literature review on DDI databases and summaries of products characteristics (SmPC) was done. Drug–drug interactions of 257 antineoplastic and supportive care drugs with direct oral anticoagulants (DOACs), warfarin, enoxaparin, or fondaparinux were categorized as no clinically significant expected DDI, potentially weak DDI, potentially clinically significant DDI, and recommendation against coadministration. Logistic regression models were performed to analyze the association between the dependent variable potentially clinically significant interaction/recommendation against coadministration and the mechanisms of DDI. Of the 1799 associations, 84.4% were absence of DDI, 3.6% potentially weak DDI, 10.2% potentially clinically relevant DDI, and 2.0% recommendation against coadministration. Warfarin has higher DDI potential than other anticoagulants. Enoxaparin and fondaparinux have fewer DDI than others. There was no difference between DOACs. Drug–drug interactions with apixaban and rivaroxaban was independently associated with the absence of CYP3A4 competition, P-glycoprotein inhibition, CYP3A4 induction, and drug class of tyrosine kinase inhibitors. Drug–drug interactions with dabigatran and edoxaban was associated with inhibition of P-glycoprotein and tyrosine kinase inhibitors. Warfarin, induction of CYP3A4, and inhibition of CYP2C9. Enoxaparin and fondaparinux, only tyrosine kinase inhibitors. Direct oral anticoagulants did not differ regarding DDI with antineoplastic agents. Warfarin presented more DDI than other anticoagulants. P-glycoprotein inhibition and CYP3A4 induction were independently associated with DDI of antineoplastic agents with DOACs.

A pharmacokinetic-pharmacodynamic model based on multi-organ-on-a-chip for drug-drug interaction studies

In drug discovery, the emergence of unexpected toxicity is often a problem resulting from a poor understanding of the pharmacokinetics of drug-drug interactions (DDI). Organ-on-a-chip (OoC) has been proposed as an in vitro model to evaluate drug efficacy and toxicity in pharmacology, but it has not been applied to DDI studies yet. In this study, we aim to evaluate whether organ-on-a-chip technologies can be applied to DDI studies. To assess the usefulness of OoC for DDI studies, we proposed a multi-organ-on-a-chip (MOoC) with a liver part as the metabolic model and a cancer part as the drug target model, and a pharmacokinetic-pharmacodynamic (PK-PD) model describing the MOoC. An anticancer prodrug, CPT-11, was used to evaluate the drug efficacy of the metabolite in the liver part of the MOoC. To evaluate DDI using the MOoC, the inhibitory effect of simvastatin and ritonavir on the metabolism of CPT-11 was tested. The DDI estimation method was evaluated by comparing the results of the concomitant administration experiment using the MOoC and the results of simulation using the proposed PK-PD model with the estimated parameters. The results were similar, suggesting that the combination of the PK-PD model and the MOoC is a useful way to predict DDI. We conclude that OoC technologies could facilitate a better understanding of pharmacokinetic mechanisms with DDI.

Concomitant medications and immune checkpoint inhibitor therapy for cancer: causation or association?

The majority of cancer patients assume concomitant medications for the treatment of cancer-related symptoms or co-morbidities. As immune checkpoint inhibitors expand in the treatment of a widening range of malignancies, drug-drug interactions have become an area of increasing interest due to the potential for some concomitant medications to exert immune-modulatory effects and influence outcomes from immunotherapy. Here, we review the evidence supporting this association across selected drug classes including antibiotics, proton pump inhibitors, metformin, and opioids.

Review of nutritional status, body composition, and effects of antineoplastic drug disposition

The overall survival for children with cancer in high income countries is excellent. However, there are many disparities that may negatively affect survival, which are particularly problematic in low income countries, such as nutritional status at diagnosis and throughout therapy. Nutritional status as well as concomitant foods, supplements, and medications may play a role in overall exposure and response to chemotherapy. Emerging science around the microbiome may also play a role and should be further explored as a contributor to disease progression and therapeutic response. This article highlights some of these issues and proposes additional areas of research relevant to nutritional status and pharmacology that are needed in pediatric oncology.

Supportive care medications coinciding with chemotherapy among children with hematologic malignancy

Pharmacokinetic (PK) conflicts can arise between supportive care medications (SCM) and chemotherapy in children with hematologic malignancy (HM). In this retrospective study, medical records for children (28 days-18 years) diagnosed with HM and receiving an SCM antimicrobial were collected from a hospital network between 1 May 2000 and 31 December 2014. PK drug-gene associations were obtained from a curated pharmacogenomics database. Among 730 patients (median age of 7.5 (IQR 3.7-13.9) years), primarily diagnosed with lymphoid leukemia (52%), lymphoma (28%), or acute myeloid leukemia (16%), chemotherapy was administered in 2846 hospitalizations. SCM accounted for 90.5% (n = 448) of distinct drugs with 93% (n = 679) of children, receiving ≥5 different SCM/hospitalization. Same-day SCM/chemotherapeutic PK gene overlap occurred in 48.3% of hospitalizations and was associated with age (p = 0.026), number of SCM, HM subtype, surgery, and hematopoietic stem cell transplant (p < 0.0001). A high and variable SCM burden among children with HM receiving chemotherapy poses a risk for unanticipated PK conflicts.

Unveiling the Impact of Morphine on Tamoxifen Metabolism in Mice in vivo

Background: Tamoxifen is used to treat breast cancer and cancer recurrences. After administration, tamoxifen is converted into two more potent antitumor compounds, 4OH-tamoxifen and endoxifen by the CYP3A4/5 and 2D6 enzymes in human. These active compounds are inactivated by the same UDP-glucuronosyltransferase isoforms as those involved in the metabolism of morphine. Importantly, cancer-associated pain can be treated with morphine, and the common metabolic pathway of morphine and tamoxifen suggests potential clinically relevant interactions.

Methods: Mouse liver microsomes were used to determine the impact of morphine on 4OH-tamoxifen metabolism in vitro. For in vivo experiments, female mice were first injected with tamoxifen alone and then with tamoxifen and morphine. Blood was collected, and LC-MS/MS was used to quantify tamoxifen, 4OH-tamoxifen, N-desmethyltamoxifen, endoxifen, 4OH-tamoxifen-glucuronide, and endoxifen-glucuronide.

Results:In vitro, we found increased K_m values for the production of 4OH-tamoxifen-glucuronide in the presence of morphine, suggesting an inhibitory effect on 4OH-tamoxifen glucuronidation. Conversely, in vivo morphine treatment decreased 4OH-tamoxifen levels in the blood while dramatically increasing the formation of inactive metabolites 4OH-tamoxifen-glucuronide and endoxifen-glucuronide.

Conclusions: Our findings emphasize the need for caution when extrapolating results from in vitro metabolic assays to in vivo drug metabolism interactions. Importantly, morphine strongly impacts tamoxifen metabolism in mice. It suggests that tamoxifen efficiency could be reduced when both drugs are co-administered in a clinical setting, e.g., to relieve pain in breast cancer patients. Further studies are needed to assess the potential for tamoxifen-morphine metabolic interactions in humans.

Harnessing the therapeutic potential of anticancer drugs through amorphous solid dispersions

The treatment of cancer is still a major challenge. But tremendous progress in anticancer drug discovery and development has occurred in the last few decades. However, this progress has resulted in few effective oncology products due to challenges associated with anticancer drug delivery. Oral administration is the most preferred route for anticancer drug delivery, but the majority of anticancer drugs currently in product pipelines and the majority of those that have been commercially approved have inherently poor water solubility, and this cannot be mitigated without compromising their potency and stability. The poor water solubility of anticancer drugs, in conjunction with other factors, leads to suboptimal pharmacokinetic performance. Thus, these drugs have limited efficacy and safety when administered orally. The amorphous solid dispersion (ASD) is a promising formulation technology that primarily enhances the aqueous solubility of poorly water-soluble drugs. In this review, we discuss the challenges associated with the oral administration of anticancer drugs and the use of ASD technology in alleviating these challenges. We emphasize the ability of ASDs to improve not only the pharmacokinetics of poorly water-soluble anticancer drugs, but also their efficacy and safety. The goal of this paper is to rationalize the application of ASD technology in the formulation of anticancer drugs, thereby creating superior oncology products that lead to improved therapeutic outcomes.

Factors associated with common mental disorders and use of psychiatric drugs in cancer outpatients

BACKGROUND

Considering the high incidence of cancer in Brazil and worldwide, the high prevalence and relevance of Common Mental Disorders (CMD) in the treatment of cancer patients, and the use of psychiatric drugs without reliably proven effectiveness, studies that contemplate this topic are needed to understand and provide rationale for the treatment of CMD in these individuals.

OBJECTIVES

This study identified prevalence and factors associated with Common Mental Disorders (CMD) and psychotropic use in cancer outpatients.

METHOD

This is a cross-sectional study with descriptive correlational design. It was developed in the chemotherapy sector of a hospital specialized in cancer. The tools used were: Self Reporting Questionnaire (SRQ-20) and structured questionnaires.

FINDINGS

Among 403 respondents, CMD prevalence was 31.5% and psychotropic use was 25.8%. CMD were associated with gender, education, family income, psychotropic use and cancer surgery. Psychotropic use was associated with gender, employment status, cancer surgery, treatment period and other physical health conditions. Logistic regressions showed CMD were associated with gender and other physical health conditions; psychotropic use was associated with gender, employment status and other conditions.

Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD.

Evaluation of the systemic and therapeutic repercussions caused by drug interactions in oncology patients

INTRODUCTION

Drug interaction is an important cause of global morbidity. It is of particular importance in cancer patients since they are often in use of polypharmacy, related to interactions between the drugs and the chemotherapeutics used.

OBJECTIVE

To evaluate the drug interaction between chemotherapy and other drugs in cancer patients.

METHODS

a cross-sectional study carried out in the outpatient oncology department of a public tertiary hospital. Two hundred thirty-five patients were included, and the drugs they were using were identified. Using the MedScape and Epocrates database, we evaluated the interactions between medications and chemotherapy by defining their frequency and dividing their severity from interaction into mild, close monitoring necessity and severe.

RESULTS

161 patients had some drug interaction. We identified 9 types of mild interactions, 23 types of interactions with close monitoring necessity, and 2 types of serious interactions. The most frequent interactions were between fluorouracil and leucovorin (32 cases) and cyclophosphamide and doxorubicin (19 cases). Serious interactions were between aspirin and pemetrexed; and leucovorin and Bactrim.

CONCLUSION

In the present study, drug interactions were frequent, including serious interactions with a potential increase in morbidity and mortality. Thus, it is necessary for oncologists to draw up a therapeutic plan considering potential interactions between prescribed chemotherapy and current medications in use by patients.