Due To Drug-drug Interactions Research Articles

Simulation of optimal dose regimens of photoactivated curcumin for antimicrobial resistance pneumonia in COVID-19 patients: A modeling approach.

Secondary antimicrobial resistance bacterial (AMR) pneumonia could lead to an increase in mortality in COVID-19 patients, particularly of geriatric patients with underlying diseases. The comedication of current medicines for AMR pneumonia with corticosteroids may lead to suboptimal treatment or toxicities due to drug-drug interactions (DDIs). This study aimed to propose new promising dosage regimens of photoactivated curcumin when co-administered with corticosteroids for the treatment of antimicrobial resistance (AMR) pneumonia in COVID-19 patients. A whole-body physiologically-based pharmacokinetic (PBPK) with the simplified lung compartments model was built and verified following standard model verification (absolute average-folding error or AAFEs). The pharmacokinetic properties of photoactivated were assumed to be similar to curcumin due to minor changes in physiochemical properties of compound by photoactivation. The acceptable AAFEs values were within 2-fold. The verified model was used to simulate new regimens for different formulations of photoactivated curcumin. The AAFEs was 1.12-fold. Original formulation (120 mg once-daily dose) or new intramuscular nano-formulation (100 mg with a release rate of 10/h given every 7 days) is suitable for outpatients with MRSA pneumonia to improve patient adherence. New intravenous formulation (2000 mg twice-daily doses) is for hospitalized patients with both MRSA and VRSA pneumonia. The PBPK models, in conjunction with MIC and applied physiological changes in COVID-19 patients, is a potential tool to predict optimal dosage regimens of photoactivated curcumin for the treatment of co-infected AMR pneumonia in COVID-19 patients. Each formulation is appropriate for different patient conditions and pathogens.

Obez Hastalarda Olası İlaç-İlaç Etkileşimlerinin Değerlendirilmesi

Aim: Polypharmacy may cause life-threatening adverse effects due to drug-drug interactions (DDIs). It is possible to observe DDIs due
 to polypharmacy in obese patients who is known to have many co-morbid diseases that necessitates multiple drug use. The aim of the
 present study is to determine the frequency and severity of potential DDIs (pDDIs) in obese patients.
 Material and Methods: This cross-sectional study analyzed the patient charts that admitted to obesity outpatient clinic of tertiary care
 hospital from April 1, 2016 to July 1, 2017. The severity of DDIs was interpreted using the Lexi-comp® drug interaction database. A chisquare
 test was performed for the comparison of the presence of DDIs based on patients’ demographic characteristics [gender (male/
 female), age categories (18-44, 45-64 and ≥65 years) and BMI (30-34.9, 35-39.9 and ≥40 kg/m2)], co-morbid clinical conditions and
 number of drugs. The comparisons were considered as statistically significant at p< 0.05.
 Results: Out of 476 patient data evaluated, a total of 781 drugs were prescribed. Among 190 patients who were prescribed two or more
 drugs, 35 (18.4%) patients had one or more pDDIs. We determined 48 (70.6%) C, 12 (17.6%) B, 7 (10.3%) D and 1 (1.5%) X risk category
 interactions. The most common pDDIs were between metformin and nonsteroidal anti-inflammatory drugs (7.4%). The presence of
 pDDIs was significantly associated with the number of prescribed drugs (p

Minimization of drug interactions in polypharmacy treatments of diabetes mellitus type 2 with cardiovascular comorbidities by using the decision support tool PM-TOM

BackgroundCombined polypharmacy treatments of multi-diseases like diabetes mellitus type 2 (DMT2) with its comorbidities could lead to serious adverse reactions (ADR) due to drug-drug interactions (DDIs). This study aimed to demonstrate that these DDI ADRs can be significantly reduced by carefully examining DDIs of recommended drugs and using advanced clinical decision support (CDS) tools, like PM-TOM (Personal Medicine: Therapy Optimization Method). MethodDMT2 with heart failure (HF) and atherosclerotic cardiovascular disease (ASCVD) were taken for analysis. First, 20 drug classes were selected, recommended in relevant medical guidelines (US, European and Canadian); for example, biguanides, sodium-glucose transporter 2 inhibitors, glucagon-like peptide-1 receptor agonists, insulins, angiotensin 2 receptor blockers, angiotensin-converting enzyme inhibitors, beta-adrenergic blockers, diuretics, and statins. Next, these classes were combined into polypharmacy treatment cases, which were organized into three groups: Basic (combinations of three drug classes), Medial (five), and Advanced (eight). Then, the tool PM-TOM was used to find treatments with minimal and maximal drug interactions (MIN-DDI and MAX-DDI) for each case. Finally, these two treatments' minimal, average and maximal DDIs were calculated and statistically analyzed to examine the scope and effects of optimizing polypharmacy treatments in each case group. ResultsIn the Basic group, 16 polypharmacy treatment cases were created; in the Medial 210 and the Advanced 736. The MIN-DDI and MAX-DDI treatments in each case group showed significant DDI differences; for example, in the Basic group, the average DDI count in the MIN-DDI treatments was 0.19 and in the MAX-DDI ones 1.75, while in the Medial and Advanced groups, these indicators were 1.66 and 7.66, and 5.76 and 20.52, respectively. Also, 87% of optimized treatments (MIN-DDI) in the Basic group showed no DDIs, 37% in the Medial, and 9% in the Advanced. In addition, 70% of cases in the Medial group had at most two DDIs, and 49% in the Advanced group at most five. ConclusionsThese findings suggest that DDI ADRs in randomly selected (unoptimized) DMT2 polypharmacy treatments can be substantially reduced using specialized decision support tools, increasing patients' chances for successful treatment and decreasing health care costs. Similar findings can be expected for other multi-diseases as well.

Drug-Drug Interactions among Patients Hospitalized with COVID-19 in Greece.

The modulation of the pharmacological action of drugs due to drug-drug interactions (DDIs) is a critical issue in healthcare. The aim of this study was to evaluate the prevalence and the clinical significance of potential DDIs in patients admitted to the University Hospital of Heraklion in Greece with coronavirus disease 2019 (COVID-19). Cardiovascular disorders (58.4%) and diabetes (types I and II) (29.6%) were the most common comorbidities. A high occurrence of DDIs was observed, and clinically significant DDIs that may hamper response to treatment represented 40.3% of cases on admission, 21% during hospitalization, and 40.7% upon discharge. Polypharmacy and comorbidities were associated with a higher prevalence of DDIs in a statistically significant way (p < 0.05, 95% CI). Clinically significant DDIs and increased C-reactive protein values upon admission were associated with prolonged hospitalization. The results reveal that patients admitted due to COVID-19 in Greece often have an additional burden of DDIs that healthcare teams should approach and resolve.

Improved prediction of drug-drug interactions using ensemble deep neural networks

Nowadays, combining multiple drugs is the optimal therapy to decelerate the pathologic process, which contains various underlying adverse effects due to drug-drug interactions (DDIs). Artificial intelligence (AI) has the potential to evaluate the interaction, pharmacodynamics, and possible side effects between drugs. Over the past years, many AI-based DDI prediction techniques, including both machine learning and deep learning, that harness available big data have been presented. Although significant progressives have been gained through previous methods, improvements are still crucial. In this work, we introduce an ensemble deep neural network that can help to improve the predictive performance of DDIs. As a result, our prediction model could predict 86 types of DDIs on a benchmark dataset with an average accuracy of 93.80%. Our ensemble classifier produces better performance when compared with the existing proposed methods on the same dataset. Our model's high performance places it among the top list of those well-built pharmacovigilance-assisted tools that facilitate the detection of DDIs to support medical decisions and drug development. We released our source codes and models at https://github.com/khanhlee/edn-ddi.

An AI-based Prediction Model for Drug-drug Interactions in Osteoporosis and Paget's Diseases from SMILES.

The skeleton is one of the most important organs in the human body in assisting our motion and activities; however, bone density attenuates gradually as we age. Among common bone diseases are osteoporosis and Paget's, two of the most frequently found diseases in the elderly. Nowadays, a combination of multiple drugs is the optimal therapy to decelerate osteoporosis and Paget's pathologic process, which comes with various underlying adverse effects due to drug-drug interactions (DDIs). Artificial intelligence (AI) has the potential to evaluate the interaction, pharmacodynamics, and possible side effects between drugs. In this research, we created an AI-based machine-learning model to predict the outcomes of interactions between drugs used for osteoporosis and Paget's treatment, which helps mitigate the cost and time to implement the best combination of medications in clinical practice. In this study, a DDI dataset was collected from the DrugBank database within the osteoporosis and Paget diseases. We then extracted a variety of chemical features from the simplified molecular-input line-entry system (SMILES) of defined drug pairs that interact with each other. Finally, machine-learning algorithms were implemented to learn the extracted features. Our stack ensemble model from Random Forest and XGBoost reached an average accuracy of 74 % in predicting DDIs. It was superior to individual models as well as previous methods in terms of most measurement metrics. This study showed the potential of AI models in predicting DDIs of Osteoporosis-Paget's disease in particular, and other diseases in general.

Risk of QT prolongation through drug interactions between hydroxychloroquine and concomitant drugs prescribed in real world practice

Hydroxychloroquine has recently received attention as a treatment for COVID-19. However, it may prolong the QTc interval. Furthermore, when hydroxychloroquine is administered concomitantly with other drugs, it can exacerbate the risk of QT prolongation. Nevertheless, the risk of QT prolongation due to drug-drug interactions (DDIs) between hydroxychloroquine and concomitant medications has not yet been identified. To evaluate the risk of QT prolongation due to DDIs between hydroxychloroquine and 118 concurrent drugs frequently used in real-world practice, we analyzed the electrocardiogram results obtained for 447,632 patients and their relevant electronic health records in a tertiary teaching hospital in Korea from 1996 to 2018. We repeated the case–control analysis for each drug. In each analysis, we performed multiple logistic regression and calculated the odds ratio (OR) for each target drug, hydroxychloroquine, and the interaction terms between those two drugs. The DDIs were observed in 12 drugs (trimebutine, tacrolimus, tramadol, rosuvastatin, cyclosporin, sulfasalazine, rofecoxib, diltiazem, piperacillin/tazobactam, isoniazid, clarithromycin, and furosemide), all with a p value of < 0.05 (OR 1.70–17.85). In conclusion, we found 12 drugs that showed DDIs with hydroxychloroquine in the direction of increasing QT prolongation.

Drug-drug interactions and potentially inappropriate medications among elderly outpatients

The high prevalence of concomitant chronic illnesses and the resulting higher number of medications in the elderly population increase the risk of adverse drug reactions due to drug-drug interactions (DDIs) and potentially inappropriate medications (PIMs). Therefore, the aim of this study was to investigate the prevalence and factors associated with DDIs and PIMs in outpatient geriatrics. In this cross-sectional study, 1512 prescriptions belonging to patients aged ≥65 years from five public pharmacies in Tehran were evaluated. Clinically relevant (C, D, and X) and significant DDIs (D and X) were documented according to the Lexicomp®. Additionally, Zhan criteria were used to detect PIMs. At least one clinically relevant DDI was detected in 61.7% of the prescriptions containing ≥2 medications. The largest percentage of prescriptions with DDIs was prescribed by cardiologists (74.3%). The number of medications in prescriptions and the specialty of the prescriber significantly affected both clinically relevant and significant DDIs in a logistic regression model. At least one PIM was identified in 16.3% of the prescriptions. General practitioners (GPs) were the largest prescribers of PIMs. The mean number of medications was significantly higher in prescriptions with PIMs. In conclusion, clinically relevant DDIs are frequent in the elderly. In terms of PIMs, more attention should be paid to the education of GPs.

Serotonin syndrome by drug interactions with linezolid: clues from pharmacovigilance-pharmacokinetic/pharmacodynamic analysis

PurposeTo characterize the post-marketing reporting of serotonin syndrome (SS) due to drug-drug interactions (DDIs) with linezolid and investigate the relationship with pharmacokinetic/pharmacodynamic (PK/PD) properties of serotonergic agents.MethodsWe queried the worldwide FDA Adverse Event Reporting System to extract SS records due to DDIs where linezolid was reported as suspect. For each serotonergic agent concomitantly reported, proportion of SS reports and mean number of DDIs were calculated and three different “SS reporting zones” were created. Relevant PK (peak concentration, area under plasma concentration curve, volume of distribution (VD), and lipophilicity) and PD (values of binding affinity (Ki) and IC50 for serotonin reuptake transporter (SERT) and 5-HT2A) parameters were extracted for each serotonergic agent, and relevant PK/PD indexes were calculated to assess correlation with mean number of DDIs (PV index).ResultsSix hundred sixty-nine reports of SS mentioning linezolid were found, being linezolid-citalopram (N = 69; 10.3%) the most frequently DDI reported. Citalopram and methadone showed respectively the highest proportion of SS reports (0.28%) and the lowest mean number of DDIs (1.41). Citalopram, escitalopram, and methadone emerged as red (i.e., alert)-zone medications: they exhibited high lipophilicity and large VD (proxies of excellent central nervous system penetration) coupled with high potency. Among PK/PD indexes, a significant correlation with PV index was found for VD/Ki SERT ratio (p = 0.05).DiscussionOur integrated approach suggests that linezolid is more likely to cause SS when co-administered with citalopram, escitalopram, and methadone, as inferred from their pharmacological properties. Proper management of SS should be tailored on a case-by-case basis.

The increased risk of bleeding due to drug-drug interactions in patients administered direct oral anticoagulants

IntroductionDirect oral anticoagulants (DOACs) have the potential to increase bleeding due to drug-drug interactions (DDIs). In the present study, the risk of bleeding was evaluated when drugs with potential DDIs were simultaneously prescribed with DOACs. Materials and methodsThe present study included patients with non-valvular atrial fibrillation (AF) and venous thromboembolism (VTE) who were newly prescribed DOACs between January 2014 and December 2016. ResultsThe study included 115,362 patients with AF or VTE who were newly administered DOACs (median age, 73 years, range, 19–108 years; males, 53.0%; AF, 81.9%). A total of 7001 any bleeding (6.1%) and 2283 major bleeding (2.0%) events occurred with DOAC prescriptions. Based on multiple logistic regression analysis, the number of DDIs was significantly associated with bleeding events independent of CHA2DS2-VASc score and Charlson Comorbidity Index (CCI). The rates of exposure to DDI drugs associated with any bleeding and major bleeding were 56.7% and 66.1%, respectively. The most common DDI drugs showed similar distributions in any or major bleeding; non-steroidal anti-inflammatory drugs (NSAIDs), antiplatelet agents, diltiazem, and amiodarone were frequently prescribed. ConclusionsPhysicians prescribing DOACs for AF or VTE should be aware of the increasing risk of bleeding associated with drugs having potential DDIs regardless of comorbidities.

Using pH Gradient Dissolution with In-Situ Flux Measurement to Evaluate Bioavailability and DDI for Formulated Poorly Soluble Drug Products.

This study described a pH-gradient dissolution method combined with flux measurements as an in vitro tool for assessing the risk of bioavailability reduction due to drug-drug interactions (DDI) caused by acid reducing agents (ARAs). The device incorporates absorption chambers into USP II dissolution vessels, with fiber optic UV-probes monitoring concentration in situ. Dosage forms of Genentech BCS class II drugs, GDC-0810, GDC-0941, and compound A, were tested by starting the dissolution in either pH 1.6 or pH 4.0 media then converting to FaSSIF after 30min. GDC-0810 showed no significant difference in flux between the two conversion experiments. A supersaturation phase was observed for GDC-0941 in the pH 1.6 experiments after media conversion to FaSSIF; however, it did not appear to occur in the pH 4.0 experiment due to low drug solubility at pH 4.0, resulting in a 95% decrease in flux compared to pH 1.6 experiment. The extent of flux reduction and the total accumulated API mass in the absorption chamber agreed well with the 89% reduction in mean Cmax and the 82% reduction in mean AUC from dog PK study between animals treated with pentagastrin and famotidine. Testing of the compound A optimized formulation tablets showed a 25% reduction in flux and in vitro absorbed amount by changing pH 1.6 to 4.0, correlating well with the AUC decrease in clinical studies. Good correlation between in vitro data and in vivo PK data demonstrated the applicability of the method for formulators to develop drug products mitigating DDI from ARAs.

Human hepatocytes and cytochrome P450-selective inhibitors predict variability in human drug exposure more accurately than human recombinant P450s.

Drugs metabolically eliminated by several enzymes are less vulnerable to variable compound exposure in patients due to drug-drug interactions (DDI) or if a polymorphic enzyme is involved in their elimination. Therefore, it is vital in drug discovery to accurately and efficiently estimate and optimize the metabolic elimination profile. CYP3A and/or CYP2D6 substrates with well described variability in vivo in humans due to CYP3A DDI and CYP2D6 polymorphism were selected for assessment of fraction metabolized by each enzyme (fmCYP ) in two in vitro systems: (i) human recombinant P450s (hrP450s) and (ii) human hepatocytes combined with selective P450 inhibitors. Increases in compound exposure in poor versus extensive CYP2D6 metabolizers and by the strong CYP3A inhibitor ketoconazole were mathematically modelled and predicted changes in exposure were compared with in vivo data. Predicted changes in exposure were within twofold of reported in vivo values using fmCYP estimated in human hepatocytes and there was a strong linear correlation between predicted and observed changes in exposure (r2 =0.83 for CYP3A, r2 =0.82 for CYP2D6). Predictions using fmCYP in hrP450s were not as accurate (r2 =0.55 for CYP3A, r2 =0.20 for CYP2D6). The results suggest that variability in human drug exposure due to DDI and enzyme polymorphism can be accurately predicted using fmCYP from human hepatocytes and CYP-selective inhibitors. This approach can be efficiently applied in drug discovery to aid optimization of candidate drugs with a favourable metabolic elimination profile and limited variability in patients.

Navigating Transporter Sciences in Pharmacokinetics Characterization Using the Extended Clearance Classification System.

Membrane transporters play an important role in the absorption, distribution, clearance, and elimination of drugs. Supported by the pharmacokinetics data in human, several transporters including organic anion transporting polypeptide (OATP)1B1, OATP1B3, organic anion transporter (OAT)1, OAT3, organic cation transporter (OCT)2, multidrug and toxin extrusion (MATE) proteins, P-glycoprotein and breast cancer resistance protein are suggested to be of clinical relevance. An early understanding of the transporter role in drug disposition and clearance allows reliable prediction/evaluation of pharmacokinetics and changes due to drug-drug interactions (DDIs) or genetic polymorphisms. We recently proposed an extended clearance classification system (ECCS) based on simple drug properties (i.e., ionization, permeability, and molecular weight) to predict the predominant clearance mechanism. According to this framework, systemic clearance of class 1B and 3B drugs is likely determined by the OATP-mediated hepatic uptake. Class 3A and 4 drugs, and certain class 3B drugs, are predominantly cleared by renal, wherein, OAT1, OAT3, OCT2, and MATE proteins could contribute to their active renal secretion. Intestinal efflux and uptake transporters largely influence the oral pharmacokinetics of class 3A, 3B, and 4 drugs. We discuss the paradigm of applying the ECCS framework in mapping the role of clinically relevant drug transporters in early discovery and development; thereby implementing the right strategy to allow optimization of drug exposure and evaluation of clinical risk due to DDIs and pharmacogenomics.

Drug-drug interaction potential in men treated with enzalutamide: Mind the gap.

Metastatic castration-resistant prostate cancer (mCRPC) patients are generally older patients with several co-morbidities and are therefore at increased risk of complications due to drug-drug interactions (DDIs). We assessed the prevalence of potential DDIs in a cohort of mCRPC patients treated with enzalutamide. We conducted a retrospective review of pharmacy records to retrieve individual drug histories of mCRPC patients who started enzalutamide therapy in a tertiary care setting. Potential DDIs were analysed using two international drug interaction compendia: Lexicomp® and Micromedex® , and the Dutch drug database. Two potential pharmacodynamic DDIs were analysed. A total of 105 records were evaluated for potential DDIs with enzalutamide. Of 205 different co-medications, 56 were flagged by at least one of the three compendia: Lexicomp, Micromedex and the Dutch drug database flagged for potential DDIs in 85%, 54% and 32%, respectively. Eighty-five per cent of DDIs were classified as major. The median number of co-medications per patient was 11 (range 1-26). The median (range) number of interactions per patient was 4 (0-10), 1 (0-5) and 0 (0-2) for Lexicomp, Micromedex and the Dutch drug database, respectively. In 23% and 45% of all patients, a potential DDI was found with PPIs and CNS depressants, respectively. A high prevalence of potential DDIs was found. The inclusion and grading of potential DDIs was highly variable between the three drug interaction compendia. Physicians, nurses and pharmacists should be aware of this potential problem, which might require intensive monitoring or alternative treatment strategies to prevent suboptimal treatment of the co-morbidities in patients treated with enzalutamide.

Clinically relevant potential drug-drug interactions among outpatients: A nationwide database study.

Adverse drug events due to drug-drug interactions (DDIs) represent a considerable public health burden, also in Slovenia. A better understanding of the most frequently occurring potential DDIs may enable safer pharmacotherapy and minimize drug-related problems. The aim of this study was to evaluate the prevalence and predictors of potential DDIs among outpatients in Slovenia. An analysis of potential DDIs was performed using health claims data on prescription drugs from a nationwide database. The Lexi-Interact Module was used as the reference source of interactions. The influence of patient-specific predictors on the risk of potential clinically relevant DDIs was evaluated using logistic regression model. The study population included 1,179,803 outpatients who received 15,811,979 prescriptions. The total number of potential DDI cases identified was 3,974,994, of which 15.6% were potentially clinically relevant. Altogether, 9.3% (N=191,213) of the total population in Slovenia is exposed to clinically relevant potential DDIs, and the proportion is higher among women and the elderly. After adjustment for cofactors, higher number of medications and older age are associated with higher odds of clinically relevant potential DDIs. The burden of DDIs is highest with drug combinations that increase risk of bleeding, enhance CNS depression or anticholinergic effects or cause cardiovascular complications. The current study revealed that 1 in 10 individuals in the total Slovenian population is exposed to clinically relevant potential DDIs yearly. Taking into account the literature based conservative estimate that approximately 1% of potential DDIs result in negative health outcomes, roughly 1800 individuals in Slovenia experience an adverse health outcome each year as a result of clinically relevant potential interactions alone.

Interaction of Rifampin and Darunavir-Ritonavir or Darunavir-Cobicistat In Vitro.

Treatment of HIV-infected patients coinfected with Mycobacterium tuberculosis is challenging due to drug-drug interactions (DDIs) between antiretrovirals (ARVs) and antituberculosis (anti-TB) drugs. The aim of this study was to quantify the effect of cobicistat (COBI) or ritonavir (RTV) in modulating DDIs between darunavir (DRV) and rifampin (RIF) in a human hepatocyte-based in vitro model. Human primary hepatocyte cultures were incubated with RIF alone or in combination with either COBI or RTV for 3 days, followed by coincubation with DRV for 1 h. The resultant DRV concentrations were quantified by high-performance liquid chromatography with UV detection, and the apparent intrinsic clearance (CLint.app.) of DRV was calculated. Both RTV and COBI lowered the RIF-induced increases in CLint.app. in a concentration-dependent manner. Linear regression analysis showed that log10 RTV and log10 COBI concentrations were associated with the percent inhibition of RIF-induced elevations in DRV CLint.app., where β was equal to -234 (95% confidence interval [CI] = -275 to -193; P < 0.0001) and -73 (95% CI = -89 to -57; P < 0.0001), respectively. RTV was more effective in lowering 10 μM RIF-induced elevations in DRV CLint.app. (half-maximal [50%] inhibitory concentration [IC50] = 0.025 μM) than COBI (IC50 = 0.223 μM). Incubation of either RTV or COBI in combination with RIF was sufficient to overcome RIF-induced elevations in DRV CLint.app., with RTV being more potent than COBI. These data provide the first in vitro experimental insight into DDIs between RIF and COBI-boosted or RTV-boosted DRV and will be useful to inform physiologically based pharmacokinetic (PBPK) models to aid in optimizing dosing regimens for the treatment of patients coinfected with HIV and M. tuberculosis.

Addressing drug-drug and drug-food interactions through personalized empowerment services for healthcare.

Personalized healthcare systems support the provision of timely and appropriate information regarding healthcare options and treatment alternatives. Especially for patients that receive multi-drug treatments a key issue is the minimization of the risk of adverse effects due to drug-drug interactions (DDIs). DDIs may be the result of doctor prescribed drugs but also due to self-medication of conventional drugs, alternative medicines, food habits, alcohol or smoking. It is therefore crucial for personalized health systems, apart from assisting physicians for optimal prescription practices, to also provide appropriate information for individual users for drug-drug interactions or similar information regarding risks for modulation of the ensuing treatment. In this manuscript we describe a DDI service including drug-food, drug-herb and other lifestyle-related factors, developed in the context of a personalized patient empowerment platform. The solution enables guidance to patients for their medication on how to reduce the risk of unwanted drug interactions and side effects in a seamless and transparent way. We present and analyze the implemented services and provide examples on using an alerting service to identify potential DDIs in two different chronic diseases, congestive heart failure and osteoarthritis.

Role of Hepatic Drug Transporters in Drug Development.

Hepatic drug transporters can play an important role in pharmacokinetics and the disposition of therapeutic drugs and endogenous substances. Altered function of hepatic drug transporters due to drug-drug interactions (DDIs), genetic polymorphisms, and disease states can often result in a change in systemic and/or tissue exposure and subsequent pharmacological/toxicological effects of their substrates. Regulatory agencies including the US Food and Drug Administration, European Medicines Agency, and Japan Pharmaceuticals and Medical Devices Agency have issued guidance for industry on drug interaction studies, which contain comprehensive recommendations on in vitro and in vivo study tools and cutoff values to evaluate the DDI potential of new molecular entities mediated by hepatic drug transporters. In this report we summarize the latest regulatory and scientific progress of hepatic drug transporters in clinical DDIs, pharmacogenetics, drug-induced liver injury (DILI), as well as methods for predicting transporter-mediated pharmacokinetics and DDIs.

A 6-year retrospective study of adverse drug reactions due to drug-drug interactions between nervous system drugs

The primary objective of this study was to determine the frequency and characteristics of adverse drug reactions (ADRs) due to drug-drug interactions (DDIs) between nervous system drugs recorded for hospitalized patients in China. The secondary objective was to identify and record the possible mechanisms underlying these DDIs. In this retrospective study performed from January 2007 to December 2012, we detected and analyzed ADRs caused by potential or actual DDIs between nervous system drugs, by using the Center of Adverse Drug Reaction Monitoring, Bengbu Food and Drug Administration (CADRMBFDA) database. The CADRMBFDA database contained 1,207 reports of ADRs due to nervous system drugs, involving 1,079 hospitalized patients. Of the ADRs reported, 131 (12.14%) were associated with potential and actual DDIs. There were 259 (21.46% of the total ADR reports) reports on potential and actual DDIs. The proportion of serious ADRs (6 out of 131) was significantly higher among actual DDI reports (p < 0.001) than among the remaining reports (6 out of 942). The results of our study confirmed that the CADRMBFDA database was a valuable resource for detecting actual DDIs. Moreover, the database helps identify drugs that can cause serious ADRs, thus indicating focus areas for healthcare education.

Potential drug-drug interactions and admissions due to drug-drug interactions in patients treated in medical departments

Adverse drug reactions due to drug-drug interactions (DDIs) are important in drug safety. The aim of this study was to check potential DDIs (pDDIs) on hospital admission and discharge and to evaluate admissions due to DDIs in medical departments of a primary city and tertiary referral hospital. Age, sex, presence of renal and liver failure, drug information, diagnosis, and urgency and reason for admission were retrospectively recorded in 520 randomly selected patients in medical departments of the University Medical Center Ljubljana. The screening program Drug-Reax was used to check for pDDIs in patients with drug information on both admission and discharge home, and the proportion of patients admitted as the consequence of a DDI was estimated. Overall, 14.6% (76/520) of patients had incomplete information on drug names in their medical documentation on admission; at the end of treatment 12.5% (52/416) of patients were discharged home with incomplete information on drug names in their discharge letters. A total of 323 patients had complete information on drug names on both admission and discharge and were included in the analysis of pDDIs: 51% (166/323) of patients on admission and 63% (204/323) on discharge had at least one pDDI (P = 0.001). Major pDDIs were found in 13% (41/323) of patients on admission and 18% (59/323) on discharge (P = 0.001). An ACE inhibitor combined with spironolactone was the most common major pDDI, representing 20.0% of all pDDIs on admission and 25.6% on discharge. Among patients with pDDI on admission, 2.4% (4/166) of were admitted because of an ADR caused by a DDI. Overall, 1.2% (4/323) of patients were admitted as the consequence of a DDI. The information on patient medication on hospital admission and discharge is incomplete. Half of patients on admission and almost two-thirds on discharge had pDDIs. ADRs due to DDIs caused 1.2% of admissions to medical departments in Ljubljana's primary city and tertiary referral hospital.