Drug Interaction Database Research Articles

DRUG-DRUG INTERACTIONS IN PSYCHIATRIC PATIENTS: IDENTIFYING HIGH-RISK COMBINATIONS AND MITIGATION STRATEGIES

Psychiatric patients are at a high risk of severe adverse drug events (ADEs) due to potential drug-drug interactions (DDIs). These interactions can worsen existing health conditions and complicate treatment outcomes. Objective: This study aimed to identify and evaluate the drug-drug interactions (DDIs) of psychoactive drugs among mental health patients and propose effective prevention measures. A retrospective study was conducted at the Department of Pharmacy, Lady Reading Hospital, Peshawar, from January 1, 2021, to July 31, 2021. The study included all psychiatric patients treated during this period. Medication records were re-examined to identify drug-drug interactions (DDIs) using reliable drug interaction databases. Adverse effects resulting from these interactions were documented. Statistical analysis assessed the frequency and severity of the identified DDIs. Mitigation strategies were developed based on the probability and clinical relevance of the interactions. These strategies included drug adjustments, regular monitoring of patients, and patient education aimed at reducing the risk of adverse effects. The major DDIs frequently occur with commonly used psychiatric medications such as antipsychotics, antidepressants, and mood stabilisers. Common adverse effects included sedation, cardiovascular complications, and increased risk of serotonin syndrome. Mitigation measures, such as drug adjustment, regular monitoring, and patient education, were suggested to reduce the risk of adverse effects. Psychiatric patients are predisposed to severe ADEs due to DDIs, which are often difficult to predict. Mitigation measures such as appropriate drug adjustments and patient education are essential to enhance patient safety and treatment outcomes.

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

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. 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. 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. 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.

Prevalence of potential drug − drug interactions in the cardiothoracic intensive care unit patients in a Chinese tertiary care teaching hospital

BackgroundWith an increasing number of reviews describing clinically significant drug–drug interactions (DDIs), the scope and severity of interactions involving commonly used drugs in cardiothoracic intensive care units (CCUs) remain unclear. This study aims to identify risk factors and determine the incidence of potential DDIs in intensive care units.MethodsDDIs were identified based on the profile of the prescribed drug and classified according to the Micromedex drug interaction database. Potential risk factors associated with DDIs have been identified.ResultsA total of 3193 medication episodes were evaluated, and 680 DDIs (21.3%) were found. A total of 203 patients were recruited into the study, with an average of 3.4 DDIs per patient [95% confidence interval (3.2 − 3.6)]. A total of 84.2% of the patients experienced at least one DDI. Anticoagulant and antiplatelet agents were involved in 33.5% (228/680) of the potential drug − drug interactions in the CCU. Univariate analysis and multiple logistic regression analysis showed that the age of the patient and the number of medications prescribed were significantly correlated with the occurrence of DDIs. In multiple linear regression analysis, the number of DDIs had a significant correlation only with the number of prescription drugs.ConclusionsA high prevalence of DDIs was observed, especially in intensive care units without pharmacist intervention and computerized drug monitoring systems, highlighting the need for active surveillance to prevent potential adverse events.

Clinically significant drug interactions between antiretroviral and co-prescribed drugs in HIV infected patients: retrospective cohort study.

IntroductionThere are limited data on human immunodeficiency viruses (HIV) infected people in the UAE and the Gulf region. This study aimed at assessing the prevalence and risk factors for potential clinically significant drug interactions (CSDIs) in a cohort of 181 HIV infected people in Dubai.MethodsA retrospective study was conducted at the outpatient infectious diseases clinic of Rashid hospital. Consecutive HIV seropositive people on anti-retroviral therapy (ART) were included. All potential CSDIs were analyzed and classified using Liverpool HIV drug interactions database.ResultsNucleoside reverse transcriptase inhibitors (NRTIs) and integrase strand transfer inhibitors (INSTIs) were the most frequently used antiretroviral agents (ARVs), while the most common (non-ARV) were cardiovascular medication followed by antilipidemic statins. A total of 140 potential CSDIs were found in nearly half (n=86, 47.5%) of the 181 included HIV persons. Of the 140 potential CSDIs, 27 (19%) were of weak clinical relevance, 108 (77%) were of potential clinical relevance, and 5 (4%) were of contraindicated clinical relevance interactions. Moreover, 52 (37.14%) of CSDIs were between two ARVs and 88 (62.85%) were between ARV and non-ARV drugs. In the univariate analysis, age, dyslipidemia, number of medications, analgesics use, statin use, supplement intake, time since diagnosis of HIV, number of ART, and use of a protease inhibitor (PI) were significant. In the logistic regression, factors independently associated with CSDIs were the number of medications (odds ratio [OR] 1.165, 95% CI 1.021–1.329, P = 0.023) and the time since diagnosis of HIV (OR 1.156, 95% CI 1.008–1.327, P = 0.038).ConclusionThe frequency of CSDIs between ART and co-medications is high in HIV seropositive people. Awareness of the risk factors may assist clinicians to recognize and manage CSDIs.

Mutation burden analysis of six common mental disorders in African Americans by whole genome sequencing.

Mental disorders present a global health concern and have limited treatment options. In today's medical practice, medications such as antidepressants are prescribed not only for depression but also for conditions such as anxiety and attention deficit hyperactivity disorder (ADHD). Therefore, identifying gene targets for specific disorders is important and offers improved precision. In this study, we performed a genetic analysis of six common mental disorders-ADHD, anxiety, depression, delays in mental development, intellectual disabilities (IDs) and speech/language disorder-in the ethnic minority of African Americans (AAs) using whole genome sequencing (WGS). WGS data were generated from blood-derived DNA from 4178 AA individuals, including 1384 patients with the diagnosis of at least one mental disorder. Mutation burden analysis was applied based on rare and deleterious mutations in the AA population between cases and controls, and further analyzed in the context of patients with single mental disorder diagnosis. Certain genes uncovered demonstrated significant P-values in mutation burden analysis. In addition, exclusive recurrences in specific type of disorder were scanned through gene-drug interaction databases to assess for availability of potential medications. We uncovered 15 genes harboring deleterious mutations, including 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR) and Uronyl 2-Sulfotransferase (UST) for ADHD; Farnesyltransferase, CAAX Box, Beta (FNTB) for anxiety; Xin Actin Binding Repeat Containing 2 (XIRP2), Natriuretic Peptide C (NPPC), Serine/Threonine Kinase 33 (STK33), Pannexin 1 (PANX1) and Neurotensin (NTS) for depression; RUNX Family Transcription Factor 3 (RUNX3), Tachykinin Receptor 1 (TACR1) and NADH:Ubiquinone Oxidoreductase Core Subunit S7 (NDUFS7) for delays in mental development; Hepsin (HPN) for ID and Collagen Type VI Alpha 3 Chain (COL6A3), Damage Specific DNA Binding Protein 1 (DDB1) and NADH:Ubiquinone Oxidoreductase Subunit A11 (NDUFA11) for speech/language disorder. Taken together, we have established critical insights into the development of new precision medicine approaches for mental disorders in AAs.

A Study of Potential drug-drug interactions in HIV positive individuals with Co-morbidities

The high prevalence of comorbid conditions, both communicable and non-communicable, in HIV patients dictate the need for prescription of additional medications which makes way for the possibility of a particular medication altering the intensity of the pharmacotherapeutic effect of a concomitantly administered medication, a potential drug-drug interaction (PDDI), which can lead to the precipitation of adverse effects and even treatment failure. In this retrospective observational study, data was collected from medical records of adult HIV positive patients on antiretroviral therapy (ART) with comorbidities who visited the hospital between January, 2015 and June, 2017. Using the University of Liverpool drug-interaction database, the identified interactions were classified into three categories. The category of PDDI requiring monitoring, dose adjustment or adjustment of time of dosing was taken and evaluated for the type of interaction, risk and prevalence. Time of onset and severity was determined using Micromedex drug-interaction database. Data analysis was done using descriptive statistics and binomial logistic regression was used for risk estimation. In this study, a total of 244 people were enrolled. A total of 711 PDDIs were identified. By analysing risk ratio, patients with more than 5 concomitant medications, protease inhibitor-based ART regimen, duration of illness (≥6 years), cardiovascular disease and presence of coinfections were at a higher risk of development of PDDIs. Pharmacokinetic PDDIs (87.2%) were higher in comparison to pharmacodynamic PDDIs (12.8%). Awareness among prescribers of this silent but important occurrence will help in recognition, prevention and management of PDDI which might otherwise complicate the therapeutic outcome.

Prevalence and predictors of clinically significant statin–drug interactions among Yemeni patients taking statins for primary and secondary prevention of cardiovascular disease

Background Statins are extensively used in clinical practice for the primary and secondary prevention of cardiovascular diseases. Statins are usually taken in combination with other medications. This may increase the risk of statin-drug interactions. The study aimed to evaluate the prevalence, patterns, and predictors of clinically significant statin-drug interactions among patients on statin therapy. Material and Methods A cross-sectional study was conducted at the cardiology, endocrine, and internal medicine outpatient clinics in four tertiary care hospitals in Sana’a, Yemen. Lexicomp Drug Interaction database was used to analyze the prescriptions for potential statin-drug interactions. Binary and multivariable logistic regression were utilized for analysis. Results Of the total number of patients (634), 114 individuals (18%) had a total of 122 statin-drug interactions. According to Lexicomp risk classification, 102 (83.6%) DDIs were class C (monitor therapy), 19 (15.6%) were class D (therapy modification), and only one (0.8%) class X (avoid combination). Simvastatin use was significantly associated with the presence of category D and X DDIs (15.9% vs. 1.6%, p < .001). Polypharmacy (OR = 2.571, p < .001) and having ≥3 comorbidities (OR = 2.512, p < .001) were the only variables associated with the presence of statin-drug interactions (C, D, and/or X). Conclusion Patients with polypharmacy and those with three or more comorbidities had a higher risk for statin-drug interactions. Therefore, routine screening by physicians and pharmacists for potential interactions should occur before prescribing or dispensing any medication to avoid clinically significant statin-drug interactions.

Potential drug-drug interactions between antiretroviral drugs and comedications, including dietary supplements, among people living with HIV: A clinical survey.

Age-related comorbidities, polypharmacy and thereby the risk of potential drug-drug interactions (PDDIs) among people living with HIV (PLWH) have increased over the years. We estimated the prevalence of comedications, including dietary supplements, and evaluated PDDIs among PLWH receiving antiretroviral therapy (ART) in Denmark in an outpatient setting. Information on prescription medication, over-the-counter medication and dietary supplements was obtained from adult PLWH receiving ART attending two outpatient clinics in Denmark. The PDDIs were identified using the University of Liverpool's drug interaction database. Associations between PDDIs and relevant variables were compared using logistic regression models. A total of 337 PLWH receiving ART with a median age of 53 years (interquartile range: 45-61) were included; 77% were male and 96% had a HIV-RNA viral load < 50 copies/mL. Twenty-six per cent of participants received five or more comedications and 56% consumed dietary supplements. Co-administration of drugs requiring dose adjustment or monitoring was identified in the medication lists of 52% of participants, and 4.5% were on drugs that should not be co-administered. Male sex [odds ratio (OR) = 1.9, 95% confidence interval (CI): 1.0-3.4], being on a protease inhibitor (OR=4.3, 95% CI: 1.9-9.7), receiving five or more comedications (OR=3.3, 95% CI: 1.5-7.2), taking over-the-counter medications (OR=1.9, 95% CI: 1.1-3.3) and dietary supplements (OR=2.0, 95% CI: 1.2-3.3) were independent predictors of PDDIs. Potential drug-drug interactions were common among our study population Our study confirms that polypharmacy and being on a protease inhibitor-based regimen increase the risk of PDDIs considerably and highlights the importance of questioning PLWH about dietary supplement intake.

Prevalence and Severity of Potential Drug-Drug Interactions in Patients with Multiple Sclerosis with and without Polypharmacy.

Polypharmacy (PP) is a common problem in modern medicine, especially known to affect patients with chronic diseases such as multiple sclerosis (MS). With an increasing number of drugs taken, the risk of potential drug–drug interactions (pDDIs) is rising. This study aims to assess the prevalence and clinical relevance of polypharmacy and pDDIs in patients with MS. Pharmacological data of 627 patients with MS were entered into two drug–drug-interaction databases to determine the number and severity of pDDIs for each patient. The patients were divided into those with and without PP (total PP and prescription medication PP (Rx PP)). Of the 627 patients included, 53.3% and 38.6% had total PP and Rx PP, respectively. On average, every patient took 5.3 drugs. Of all patients, 63.8% had at least one pDDI with a mean of 4.6 pDDIs per patient. Less than 4% of all pDDIs were moderately severe or severe. Medication schedules should be checked for inappropriate medication and for possible interacting drugs to prevent pDDIs. Physicians as well as pharmacists should be more sensitive towards the relevance of pDDIs and know how they can be detected and avoided.

Drug interactions in hospital prescriptions in Denmark: Prevalence and associations with adverse outcomes.

PurposeWhile the beneficial effects of medications are numerous, drug–drug interactions may lead to adverse drug reactions that are preventable causes of morbidity and mortality. Our goal was to quantify the prevalence of potential drug–drug interactions in drug prescriptions at Danish hospitals, estimate the risk of adverse outcomes associated with discouraged drug combinations, and highlight the patient types (defined by the primary diagnosis of the admission) that appear to be more affected.MethodsThis cross‐sectional (descriptive part) and cohort study (adverse outcomes part) used hospital electronic health records from two Danish regions (~2.5 million people) from January 2008 through June 2016. We included all inpatients receiving two or more medications during their admission and considered concomitant prescriptions of potentially interacting drugs as per the Danish Drug Interaction Database. We measured the prevalence of potential drug–drug interactions in general and discouraged drug pairs in particular during admissions and associations with adverse outcomes: post‐discharge all‐cause mortality rate, readmission rate and length‐of‐stay.ResultsAmong 2 886 227 hospital admissions (945 475 patients; median age 62 years [IQR: 41–74]; 54% female; median number of drugs 7 [IQR: 4–11]), patients in 1 836 170 admissions were exposed to at least one potential drug–drug interaction (659 525 patients; median age 65 years [IQR: 49–77]; 54% female; median number of drugs 9 [IQR: 6–13]) and in 27 605 admissions to a discouraged drug pair (18 192 patients; median age 68 years [IQR: 58–77]; female 46%; median number of drugs 16 [IQR: 11–22]). Meropenem‐valproic acid (HR: 1.5, 95% CI: 1.1–1.9), domperidone‐fluconazole (HR: 2.5, 95% CI: 2.1–3.1), imipramine‐terbinafine (HR: 3.8, 95% CI: 1.2–12), agomelatine‐ciprofloxacin (HR: 2.6, 95% CI: 1.3–5.5), clarithromycin‐quetiapine (HR: 1.7, 95% CI: 1.1–2.7) and piroxicam‐warfarin (HR: 3.4, 95% CI: 1–11.4) were associated with elevated mortality. Confidence interval bounds of pairs associated with readmission were close to 1; length‐of‐stay results were inconclusive.ConclusionsWell‐described potential drug–drug interactions are still missed and alerts at point of prescription may reduce the risk of harming patients; prescribing clinicians should be alert when using strong inhibitor/inducer drugs (i.e. clarithromycin, valproic acid, terbinafine) and prevalent anticoagulants (i.e. warfarin and non‐steroidal anti‐inflammatory drugs ‐ NSAIDs) due to their great potential for dangerous interactions. The most prominent CYP isoenzyme involved in mortality and readmission rates was 3A4.

POSC204 Drug-Drug Interaction Databases: Sensitivity and Specificity to Detect Manifest Drug-Drug Interactions, Reliability Ratings and Management Strategies of Potential Drug-Drug Interactions

The objective was to compare drug-drug interaction (DDI) databases with regard to reliability ratings, severity categories and management strategies of potential DDIs and to determine the specificity and sensitivity of DDI databases to detect manifest DDI which contributed to unplanned hospital admission at the University Hospital Hradec Králové, Czech Republic.

Potential or contraindicated drug–drug interactions with antiretroviral therapy in real-world settings in Taiwan

Given the complex metabolic pathway of antiretroviral therapy (ART), polypharmacy may increase the risk of drug-drug interactions (DDIs). Therefore, we investigated the frequency of DDIs during ART exposure to improve medical care for patients with human immunodeficiency virus (HIV). This was a nationwide cross-sectional study using claims data from the National Health Insurance in Taiwan in 2016. Potential or contraindicated DDIs with recommended first-line ART (1L-ART) or protease inhibitors (PIs) were identified from the University of Liverpool drug interaction database. Fisher's exact or chi-square test was used to determine the significance of categorical variables. A total of 25,863 HIV-infected individuals were identified. Regarding 1L-ART users, patients with contraindicated DDIs accounted for 1-4%, whereas those with potential DDIs accounted for 15-50%. The most frequently coprescribed medications related to potential DDIs were diclofenac and polyvalent cation-containing antacids. Among PI users, 8-10% of them had contraindicated DDIs while 44-50% of them had potential DDIs. The medications related to potential DDIs with PIs were zolpidem, betamethasone, polyvalent cation-containing antacids, and loperamide. Our study showed a low prevalence of contraindicated DDIs in the HIV population; however, more attention should be paid to a high proportion of potential DDIs. Strategies to avoid these DDIs should be implemented if possible. Further research that focuses on the long-term clinical impact of potential DDIs is warranted.

Drug interactions with angiotensin-converting enzyme inhibitors

We reviewed potentially clinically significant drug–drug interactions (DDIs) with angiotensin-converting enzyme inhibitors (ACEIs) using Stockley’s Drug Interactions books and two online DDI databases. We identified &gt;10 such DDIs and described their mechanisms, clinical evidence and importance, as well as their management. Most DDIs with ACEIs were pharmacodynamic; only a few were pharmacokinetic. Most DDIs with ACEIs involved drugs that can lead to excessive reduction of blood pressure, hyperkalemia and impaired renal function. There is some evidence linking ACEIs with the induction of lithium toxicity. Severe hypersensitivity reactions may occur in patients treated with ACEIs with concomitant use of allopurinol or parenteral applications of iron. ACEIs should be combined carefully with hepatotoxic drugs due to their own potential hepatotoxic effects. The prevalence of adverse drug reactions due to DDIs might be high because they are prescribed frequently. Fortunately, most of such DDIs can be managed by clinical and laboratory monitoring of patients or by dosage adjustments of concomitantly used drugs.

Determination of potential drug-drug interactions in prescription orders dispensed in a community pharmacy setting using Micromedex® and Lexicomp®: a retrospective observational study.

Background Community pharmacists have a role in identifying drug-drug interactions (DDIs) when processing prescription orders and dispensing medications to patients. The harmful effects of DDIs can be prevented or minimized by using an electronic DDI checker to screen for potential DDIs (pDDIs). However, different DDI checkers have variable rates of detecting pDDIs. Aim To estimate the prevalence of pDDIs in prescriptions dispensedina community pharmacy setting using two electronic DDI databases and to evaluate the association between thepDDIs and contributory factors. Method Eligible prescription orders dispensed by a community pharmacy chain in Qatar from January to July 2020 were included in this retrospective observational study. For each prescription, Micromedex® and Lexicomp® were simultaneously used to identify pDDIs, and the interactions categorized based on severity and risk rating. Results Seven hundred-twenty prescriptions met the inclusion criteria, of which Micromedex® and Lexicomp® respectively identified 125 prescriptions (17.4%) and 230 prescriptions (31.9%) as having at least one pDDI. Moderate strength of agreement was found between Lexicomp® and Micromedex® in identifying pDDIs (Cohen's Kappa = 0.546). Micromedex® classified 61.6% of DDIs as major severity, while Lexicomp® classified 30.8% as major severity. The number of concurrent medications per prescription was significantly and positively associated with pDDI. Conclusion This study demonstrates a high prevalence of pDDIs among prescriptions dispensed in a community pharmacy setting.It is advisable that community pharmacists in Qatar, who typically do not have access to computerized patient profiles, use these DDIcheckers to ensure all pDDIs are communicated to respective prescribers for appropriate action.

Predictive In Vitro-In Vivo Extrapolation for Time Dependent Inhibition of CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP2D6 Using Pooled Human Hepatocytes, Human Liver Microsomes, and a Simple Mechanistic Static Model.

Inactivation of Cytochrome P450 (CYP450) enzymes can lead to significant increases in exposure of comedicants. The majority of reported in vitro to in vivo extrapolation (IVIVE) data have historically focused on CYP3A, leaving the assessment of other CYP isoforms insubstantial. To this end, the utility of human hepatocytes (HHEP) and human liver microsomes (HLM) to predict clinically relevant drug-drug interactions was investigated with a focus on CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP2D6. Evaluation of IVIVE for CYP2B6 was limited to only weak inhibition. A search of the University of Washington Drug-Drug Interaction Database was conducted to identify a clinically relevant weak, moderate, and strong inhibitor for selective substrates of CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP2D6, resulting in 18 inhibitors for in vitro characterization against 119 clinical interaction studies. Pooled human hepatocytes and HLM were preincubated with increasing concentrations of inhibitors for designated timepoints. Time dependent inhibition was detected in HLM for four moderate/strong inhibitors, suggesting that some optimization of incubation conditions (i.e., lower protein concentrations) is needed to capture weak inhibition. Clinical risk assessment was conducted by incorporating the in vitro derived kinetic parameters maximal rate of enzyme inactivation (min-1) (kinact) and concentration of inhibitor resulting in 50% of the maximum enzyme inactivation (KI) into static equations recommended by regulatory authorities. Significant overprediction was observed when applying the basic models recommended by regulatory agencies. Mechanistic static models, which consider the fraction of metabolism through the impacted enzyme, using the unbound hepatic inlet concentration lead to the best overall prediction accuracy with 92% and 85% of data from HHEPs and HLM, respectively, within twofold of the observed value. SIGNIFICANCE STATEMENT: Coupling time-dependent inactivation parameters derived from pooled human hepatocytes and human liver microsomes (HLM) with a mechanistic static model provides an easy and quantitatively accurate means to determine clinical drug-drug interaction risk from in vitro data. Optimization is needed to evaluate time-dependent inhibition (TDI) for weak and moderate inhibitors using HLM. Recommendations are made with respect to input parameters for in vitro to in vivo extrapolation (IVIVE) of TDI with non-CYP3A enzymes using available data from HLM and human hepatocytes.

DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety.

Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Despite the continuous information accumulation of clinically significant DDIs, there are few open-access knowledge systems dedicated to the curation of DDI associations. To facilitate the clinicians to screen for dangerous drug combinations and improve health systems, we present DDInter, a curated DDI database with comprehensive data, practical medication guidance, intuitive function interface, and powerful visualization to the scientific community. Currently, DDInter contains about 0.24M DDI associations connecting 1833 approved drugs (1972 entities). Each drug is annotated with basic chemical and pharmacological information and its interaction network. For DDI associations, abundant and professional annotations are provided, including severity, mechanism description, strategies for managing potential side effects, alternative medications, etc. The drug entities and interaction entities are efficiently cross-linked. In addition to basic query and browsing, the prescription checking function is developed to facilitate clinicians to decide whether drugs combinations can be used safely. It can also be used for informatics-based DDI investigation and evaluation of other prediction frameworks. We hope that DDInter will prove useful in improving clinical decision-making and patient safety. DDInter is freely available, without registration, at http://ddinter.scbdd.com/.

Pharmacokinetic Drug-Drug Interactions with Drugs Approved by the US Food and Drug Administration in 2020: Mechanistic Understanding and Clinical Recommendations.

Drug-drug interaction (DDI) data for small molecular drugs approved by the US Food and Drug Administration in 2020 (N = 40) were analyzed using the University of Washington Drug Interaction Database. The mechanism(s) and clinical relevance of these interactions were characterized based on information available in the new drug application reviews. About 180 positive clinical studies defined as mean area under the curve ratios (AUCRs) ≥1.25 for inhibition DDIs or pharmacogenetic studies and ≤0.8 for induction DDIs were then fully analyzed. Oncology was the most represented therapeutic area, including 30% of 2020 approvals. As victim drugs, inhibition and induction of CYP3A explained most of all observed clinical interactions. Three sensitive substrates were identified: avapritinib (CYP3A), lonafarnib (CYP3A), and relugolix (P-glycoprotein), with AUCRs of 7.00, 5.07, and 6.25 when coadministered with itraconazole, ketoconazole, and erythromycin, respectively. As precipitants, three drugs were considered strong inhibitors of enzymes (AUCR ≥ 5): cedazuridine for cytidine deaminase and lonafarnib and tucatinib for CYP3A. No drug showed strong inhibition of transporters. No strong inducer of enzymes or transporters was identified. As expected, all DDIs with AUCRs ≥5 or ≤0.2 and almost all those with AUCRs of 2-5 and 0.2-0.5 triggered dosing recommendations in the drug label. Overall, all 2020 drugs found to be either sensitive substrates or strong inhibitors of enzymes or transporters were oncology treatments, underscoring the need for effective DDI management strategies in patients with cancer often receiving polytherapy. SIGNIFICANCE STATEMENT: This minireview provides a thorough and specific overview of the most significant pharmacokinetic-based DDI data observed (or expected) with small molecular drugs approved by the US Food and Drug Administration in 2020. It will help to better understand mitigation strategies to manage the DDI risks in the clinic.

Evidence-Based Guidelines for Drug Interaction Studies: Model-Informed Time Course of Intestinal and Hepatic CYP3A4 Inhibition by Clarithromycin.

Drug-drug interaction (DDI) studies are mandated in drug development; however, protocols for evaluating the impact of cytochrome P450 (CYP) inhibition on new molecular entities are currently inconsistent. This study utilised validated physiologically based pharmacokinetic (PBPK) software to define the optimal dose, frequency, and duration of clarithromycin to achieve optimal characterisation of CYP3A4 inhibition in a study population. The Simcyp® Simulator (Version 19.0) was used to simulate clarithromycin-mediated CYP3A4 inhibition in healthy virtual cohorts. Between trial variability in magnitude and time course of CYP3A4 activity was assessed following clarithromycin dosing strategies obtained from the University of Washington Drug Interaction Database. Heterogeneity in CYP3A4 inhibition was evaluated across sex, race, and age. Literature review identified 500 mg twice daily for 5 days as the most common clarithromycin dosing protocol for CYP3A4 inhibition studies. On simulation, clarithromycin 500 mg twice daily resulted in the largest steady-state inhibition of hepatic (percent mean inhibition [95%CI] = 80 [77-83]) and small intestine (94 [94-95]) CYP3A4 activity (as compared to 500 mg once daily, 400 mg once/twice daily, or 250 mg once/twice daily). Additionally, 500 mg twice daily was associated with the shortest time for 90% of individuals to reach 90% of their minimum hepatic (4 days) and small intestine (1 days) CYP3A4 activity. The study presented herein supports that clarithromycin dosing protocol of 500 mg twice daily for 5 days is sufficient to achieve maximal hepatic and small intestine CYP3A4 inhibition. These findings were consistent between sex, race, and age differences.

Recognition and management of clinically significant drug-drug interactions between antiretrovirals and co-medications in a cohort of people living with HIV in rural Tanzania: a prospective questionnaire-based study.

The extent to which drug-drug interactions (DDIs) between antiretrovirals (ARVs) and co-medications are recognized and managed has not been thoroughly evaluated in limited-resource settings. This prospective questionnaire-based study aimed to determine the prevalence and risk factors for unrecognized/incorrectly managed DDIs in people living with HIV followed-up at the Chronic Diseases Clinic of Ifakara (CDCI) and enrolled in the Kilombero and Ulanga Antiretroviral Cohort (KIULARCO). We prospectively included ARV-treated adults receiving ≥1 co-medication coming for a follow-up visit at the CDCI between March and July 2017. Using a structured questionnaire, physicians were requested to identify potentially clinically significant DDIs in the prescribed treatment, to provide recommendations for their management and to indicate any hurdles to implement the recommendations. Prescriptions were subsequently screened for DDIs using the Liverpool DDIs database. Identified clinically significant DDIs and their recommended management according to the DDIs database were compared with the information provided in the questionnaires. Among 334 participants, the median age was 47 years (IQR = 40-56 years), 69% were female and 82% had ≥1 non-communicable disease (NCD). Overall, 129 participants had ≥1 clinically relevant DDI, which was not recognized and/or incorrectly managed in 56 participants (43%). Of those, 6 (11%) were due to limited monitoring options or medication affordability issues. In the multivariable logistic regression, the presence of ≥1 NCD was associated with an increased risk for unrecognized/incorrect DDI management (OR = 15.8; 95% CI = 1.8-139.6). Recognition/appropriate management of DDIs is suboptimal, highlighting the need for educational programmes, pharmacovigilance activities and increased access to medications and monitoring options. This should become a focus of HIV programmes given the increasing burden of NCDs in sub-Saharan Africa.

Comparison of potential psychiatric drug interactions in six drug interaction database programs: A replication study after 2years of updates.

Drug interaction database programs are a fundamental clinical tool. In 2018, we compared the category of potential drug-drug interaction (DDI) provided by six drug interaction database programs for 100 drug interaction pairs including psychiatric drugs, and found the category often differed. This study replicated the comparison in 2020 after 2years of updates to all six drug interaction database programs. The 100 drug pairs included 94 different drugs: 67 pairs with a psychiatric and non-psychiatric drug, and 33 pairs with two psychiatric drugs. The assigned category of potential DDI for the drug pairs was compared using percent agreement and Fleiss kappa statistic of interrater reliability. Despite 67 updates involving 46 of the 100 drug pairs, differences remained. The overall percent agreement among the six drug interaction database programs for the category of potential DDI was 67%. The interrater agreement results did not change. The Fleiss kappa overall interrater agreement was fair. The kappa agreement for a drug pair with any severe category rating was substantial, and the kappa agreement for a drug pair with any major category rating was fair. Physicians should be aware of the inconsistency among drug interaction database programs in the category of potential DDI for drug pairs including psychiatric drugs. Additionally, the category of potential DDI for a drug pair may change over time. This study highlights the importance of ongoing international efforts to standardize methods used to define and classify potential DDI.