Drug-drug Interaction Screening Research Articles

Detecting drug communities and predicting comprehensive drug\u2013drug interactions via balance regularized semi-nonnegative matrix factorization

BackgroundBecause drug–drug interactions (DDIs) may cause adverse drug reactions or contribute to complex-disease treatments, it is important to identify DDIs before multiple-drug medications are prescribed. As the alternative of high-cost experimental identifications, computational approaches provide a much cheaper screening for potential DDIs on a large scale manner. Nevertheless, most of them only predict whether or not one drug interacts with another, but neglect their enhancive (positive) and depressive (negative) changes of pharmacological effects. Moreover, these comprehensive DDIs do not occur at random, but exhibit a weakly balanced relationship (a structural property when considering the DDI network), which would help understand how high-order DDIs work.ResultsThis work exploits the intrinsically structural relationship to solve two tasks, including drug community detection as well as comprehensive DDI prediction in the cold-start scenario. Accordingly, we first design a balance regularized semi-nonnegative matrix factorization (BRSNMF) to partition the drugs into communities. Then, to predict enhancive and degressive DDIs in the cold-start scenario, we develop a BRSNMF-based predictive approach, which technically leverages drug-binding proteins (DBP) as features to associate new drugs (having no known DDI) with other drugs (having known DDIs). Our experiments demonstrate that BRSNMF can generate the drug communities, which exhibit more reasonable sizes, the property of weak balance as well as pharmacological significances. Moreover, they demonstrate the superiority of DBP features and the inspiring ability of the BRSNMF-based predictive approach on comprehensive DDI prediction with 94% accuracy among top-50 predicted enhancive and 86% accuracy among bottom-50 predicted degressive DDIs.ConclusionsOwing to the regularization of the weak balance property of the comprehensive DDI network into semi-nonnegative matrix factorization, our proposed BRSNMF is able to not only generate better drug communities but also provide an inspiring comprehensive DDI prediction in the cold-start scenario.

A Case-Crossover-Based Screening Approach to Identifying Clinically Relevant Drug-Drug Interactions in Electronic Healthcare Data.

We sought to develop a semiautomated screening approach using electronic healthcare data to identify drug-drug interactions (DDIs) that result in clinical outcomes. Using a case-crossover design with 30-day hazard and referent windows, we evaluated codispensed drugs (potential precipitants) in 7,801 patients who experienced rhabdomyolysis while on cytochrome P450 (CYP)3A4-metabolized statins and in 15,147 who experienced bleeding while on dabigatran. Estimates of direct associations between precipitant drugs and outcomes were used to adjust for bias and precipitants' direct effects. The P values were adjusted for multiple testing using the false discovery rate (FDR). From among 460 drugs codispensed with statins, 1 drug (clarithromycin) generated an alert (adjusted odds ratio (OR) 5.83, FDR<0.05). From among 485 drugs codispensed with dabigatran, 2 drugs (naproxen and enoxaparin, ORs 2.50 and 2.75; FDR<0.05) generated an alert. All three signals reflected known pharmacologic interactions, confirming the potential of case-crossover-based approaches for DDI screening in electronic healthcare data.

Development of a drug-drug interaction screening tool for oncology clinical trial enrollment.

315 Background: Screening drug-drug interactions (DDI) for subjects enrolling in oncology clinical trials is critical to ensuring patient safety and the validity of clinical trial data. We previously reported that DDI screening is not uniformly conducted when screening patients for enrollment into SWOG clinical trials and found that at the University of Michigan Rogel Cancer Center up to 24.2% of subjects enrolled in National Clinical Trial Network (NCTN) trials had a DDI. Screening tools aid in DDI reduction in clinical practice, but none have been created for clinical trial enrollment. Our objective was to develop a clinical trial specific DDI screening tool based on features requested by the end-users of the tool at U-M. Methods: Semi-structured and informal interviews were conducted with all data managers who enroll patients into NCTN clinical trials at the U-M cancer center. Data managers were asked about their current workflow and desired features of a DDI screening tool. Responses were combined and reviewed for feasibility. Desired features were conveyed to PEPID, LLC (Phoenix, AZ) for tool development. Results: Four data managers were interviewed. Protocol-guided screening was a key workflow feature, which was completed by gathering DDI information primarily from the exclusion criteria and drug information sections of each respective protocol, Google, CredibleMeds, and the Indiana University P450 Drug Interaction Table. Consequently, a critical feature was the display of drug characteristics with wording that aligned with that in the protocol including transporter and CYP450 substrates, inhibitors, or inducers and QT prolongation potential. Additional desirable features included separate entry of study and concomitant drugs, filtering to display only DDI with study drugs, and PDF export of results. PEPID developed a prototype tool including these desired attributes for a prospective implementation pilot study. Conclusions: A first generation clinical trial specific DDI screening tool was developed based on end-user feedback. We are designing a prospective study to determine whether implementation of this tool can reduce DDI, enhance patient safety, and ensure validity of clinical trial data.

Role of a clinical pharmacist as part of a multidisciplinary care team in the treatment of HCV in patients living with HIV/HCV coinfection.

BackgroundThe objective of the study was to evaluate the role of a clinical pharmacist in hepatitis C virus (HCV) treatment of patients living with HIV/HCV coinfection.MethodsWe conducted a descriptive study to quantify the functions of a clinical pharmacist in HCV treatment of patients living with HIV/HCV coinfection who were initiating HCV treatment at a publicly funded clinic between March 18, 2015 and September 15, 2016. The clinical pharmacist’s role was categorized into eight categories: 1) HCV prior authorization (PA) completion; 2) HCV medication adherence counseling; 3) HCV drug-drug interaction (DDI) counseling and screening; 4) HCV medication counseling regarding common adverse events (AEs); 5) HCV counseling regarding HCV treatment outcomes and risk of reinfection; 6) ordering laboratory tests and interpretation of HCV laboratory values; 7) HIV medication AE assessment; and 8) other (including refilling medications and management of other comorbidities).ResultsOne hundred and thirty-five patients initiated treatment during this timeframe: 77.0% were males, 56.3% non-cirrhotic, 77.0% HCV treatment-naïve, 45.9% HCV genotype 1a, and 83.0% initiated on ledipasvir/sofosbuvir. The clinical pharmacist completed 150 PAs, counseled on HCV medication adherence in 79.2% of patients, conducted HCV DDI counseling and screening in 54.2%, and monitored HCV medication AEs in 54.2%. The clinical pharmacist counseled patients on HCV treatment outcomes and risk of reinfection in 53.1%, ordered laboratory tests in 44.8%, and reported and interpreted laboratory values in 44.8%. The clinical pharmacist assessed HIV medication AEs in 54.2% of patients and participated in other activities in 42.7%.ConclusionA clinical pharmacist’s expertise as part of a multidisciplinary care team facilitates optimal treatment outcomes and provides critical support in the management of DAA therapy in individuals living with HIV/HCV coinfection.

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

Patients with cancer are an especially vulnerable population to potential drug-drug interactions (DDIs). This makes it important to adequately screen them for DDIs. The objective of this study was to compare the abilities of nine DDI screening tools to detect clinically relevant interactions with oral oncolytics. Subscription-based tools (ie, PEPID, Micromedex, Lexicomp, Facts & Comparisons) and free tools (ie, Epocrates Free, Medscape, Drugs.com, RxList, WebMD) were compared for their abilities to detect clinically relevant DDIs for 145 drug pairs including an oral oncology agent. Clinical relevance was determined by a pharmacist using Stockley's Drug Interactions. Descriptive statistics were calculated for each tool, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), and then compared grouped by free or subscription-based tools for the secondary analysis and analyzed via generalized estimating equations. For individual metrics, PPV had overall higher values (0.88 to 0.97) relative to the low values included for sensitivity (0.65 to 0.96), specificity (0.53 to 0.93) and NPV (0.38 to 0.83). The top-performing subscription and free tools, Lexicomp and Drugs.com, had no statistically significant differences in performance. Overall, subscription tools had a significantly higher sensitivity (0.85 ± 0.017 v 0.78 ± 0.017; P = .0082) and NPV (0.57 ± 0.039 v 0.48 ± 0.032; P = .031) than free tools. No differences were observed between the specificity and PPV. Due to the low performance of some tools for sensitivity, specificity, and NPV, individual performance should be examined and prioritized on the basis of the intended use when selecting a DDI tool. If a strong-performing subscription-based tool is unavailable, a strong-performing free option, like Drugs.com, is available.

Biomedical Informatics Approaches to Identifying Drug-Drug Interactions: Application to Insulin Secretagogues.

Drug-drug interactions with insulin secretagogues are associated with increased risk of serious hypoglycemia in patients with type 2 diabetes. We aimed to systematically screen for drugs that interact with the five most commonly used secretagogues-glipizide, glyburide, glimepiride, repaglinide, and nateglinide-to cause serious hypoglycemia. We screened 400 drugs frequently coprescribed with the secretagogues as candidate interacting precipitants. We first predicted the drug-drug interaction potential based on the pharmacokinetics of each secretagogue-precipitant pair. We then performed pharmacoepidemiologic screening for each secretagogue of interest, and for metformin as a negative control, using an administrative claims database and the self-controlled case series design. The overall rate ratios (RRs) and those for four predefined risk periods were estimated using Poisson regression. The RRs were adjusted for multiple estimation using semi-Bayes method, and then adjusted for metformin results to distinguish native effects of the precipitant from a drug-drug interaction. We predicted 34 pharmacokinetic drug-drug interactions with the secretagogues, nine moderate and 25 weak. There were 140 and 61 secretagogue-precipitant pairs associated with increased rates of serious hypoglycemia before and after the metformin adjustment, respectively. The results from pharmacokinetic prediction correlated poorly with those from pharmacoepidemiologic screening. The self-controlled case series design has the potential to be widely applicable to screening for drug-drug interactions that lead to adverse outcomes identifiable in healthcare databases. Coupling pharmacokinetic prediction with pharmacoepidemiologic screening did not notably improve the ability to identify drug-drug interactions in this case.

High prevalence of potential drug-drug interactions in patients with castration-resistant prostate cancer treated with abiraterone acetate.

Abiraterone acetate (AA), used to treat metastatic castration-resistant prostate cancer (mCRPC), inhibits androgen biosynthesis by blocking cytochrome P450 (CYP) 17A1. It also inhibits other cytochromes involved in the metabolism of various widely used medications. As such, there is presumably a high potential for drug-drug interactions (DDIs) that can diminish the efficacy of AA or concurrent medications, or increase the risk of DDI-related adverse events (AEs). However, the scale of AA-associated DDIs is currently unknown. We conducted a retrospective review of pharmacy records and electronic patient charts to retrieve individual drug histories and on-treatment AEs of mCRPC patients beginning AA therapy in a tertiary care setting. Potential DDIs were analyzed using two commercial databases, Lexicomp and Micromedex. Eighty-four informative patients were identified. Sixty-five patients (77%) and 44 patients (52%) were flagged for one or more potential DDIs by the Lexicomp and Micromedex databases, respectively. One hundred eighty-four potential DDIs were identified overall, with a median of 1 DDI per patient in both databases. Possibly due to rigorous DDI screening before AA treatment initiation, we did not identify a definite instance of DDI-related AEs. The use of commercial DDI databases suggests a substantial risk of potentially consequential DDIs in mCRPC patients undergoing AA therapy. However, prospective investigations with larger patient populations are required to better establish the clinical relevance of these DDIs.

Pharmacokinetic Evaluation of a Drug Transporter Cocktail Consisting of Digoxin, Furosemide, Metformin, and Rosuvastatin.

This article reports the clinical investigation of a probe drug cocktail containing substrates of key drug transporters. Single oral doses of 0.25 mg digoxin (P‐gp), 5 mg furosemide (OAT1 and OAT3), 500 mg metformin (OCT2, MATE1, and MATE2‐K), and 10 mg rosuvastatin (OATP1B1, OATP1B3, and BCRP) were administered separately or as a cocktail in a randomized six‐period crossover trial in 24 healthy male volunteers. As a cocktail, relative bioavailabilities of digoxin and metformin and furosemide AUC0‐tz were similar to separate dosing. However, when administered as a cocktail the Cmax of furosemide was 19.1% lower and the Cmax and AUC0‐tz of rosuvastatin were 38.6% and 43.4% higher, respectively. In addition, the effects of increased doses of metformin or furosemide on the cocktail were investigated in 11 and 12 subjects, respectively. The cocktail explored in this trial has the potential to be used for the in vivo screening of transporter‐mediated drug–drug interactions. © 2016 American Society for Clinical Pharmacology and Therapeutics

Are We Getting the Best Return on Investment From Clinical Drug-Drug Interaction Studies?

Are We Getting the Best Return on Investment From Clinical Drug-Drug Interaction Studies?

Generation of Bayesian prediction models for OATP-mediated drug–drug interactions based on inhibition screen of OATP1B1, OATP1B1∗15 and OATP1B3

Human organic anion-transporting polypeptide 1B1 (OATP1B1) and OATP1B3 are important hepatic uptake transporters. Early assessment of OATP1B1/1B3-mediated drug–drug interactions (DDIs) is therefore important for successful drug development. A promising approach for early screening and prediction of DDIs is computational modeling. In this study we aimed to generate a rapid, single Bayesian prediction model for OATP1B1, OATP1B1∗15 and OATP1B3 inhibition. Besides our previously generated HEK-OATP1B1 and HEK-OATP1B1∗15 cells, we now generated and characterized HEK-OATP1B3 cells. Using these cell lines we investigated the inhibitory potential of 640 FDA-approved drugs from a commercial library (10μM) on the uptake of [3H]-estradiol-17β-d-glucuronide (1μM) by OATP1B1, OATP1B1∗15, and OATP1B3. Using a cut-off of ⩾60% inhibition, 8% and 7% of the 640 drugs were potent OATP1B1 and OATP1B1∗15 inhibitors, respectively. Only 1% of the tested drugs significantly inhibited OATP1B3, which was not sufficient for Bayesian modeling. Modeling of OATP1B1 and OATP1B1∗15 inhibition revealed that presence of conjugated systems and (hetero)cycles with acceptor/donor atoms in- or outside the ring enhance the probability of a molecule binding these transporters. The overall performance of the model for OATP1B1 and OATP1B1∗15 was ⩾80%, including evaluation with a true external test set. Our Bayesian classification model thus represents a fast, inexpensive and robust means of assessing potential binding of new chemical entities to OATP1B1 and OATP1B1∗15. As such, this model may be used to rank compounds early in the drug development process, helping to avoid adverse effects in a later stage due to inhibition of OATP1B1 and/or OATP1B1∗15.

An overview of on‐line systems using drug metabolizing enzymes integrated into capillary electrophoresis

Enzymatic assays using CE are now among the most noteworthy applications of this analytical technique in pharmacology-related investigations. Studies of metabolic pathways of new chemical entities mediated by drug metabolizing enzymes are attracting particular attention. Conventional CE-based enzymatic in vitro assays are generally restricted to the separation of reagents after incubation performed off-line. EMMA represents an alternative and fully prospective approach, allowing injection, reaction, separation, and detection to be conducted in a single capillary. Such an on-line system-in contrast to the standard approach-enables automation, miniaturization, and a significant reduction in reagent volumes, resulting in a very robust and cost-efficient method. Hence, EMMA could be a method of choice for the screening of new drugs, enzymatic inhibitors, and putative drug-drug interactions. This review provides a summary of reports covering the area of EMMA-based and related methods implemented into in vitro studies of drug metabolizing enzymes. A general description of the EMMA framework, enzyme families, and a concise discussion of the prognosis for the development of this methodology are given as well.

Potential drug–drug interactions at a referral hematology–oncology ward in Iran: a cross-sectional study

To assess the pattern and probable risk factors for moderate and major drug-drug interactions in a referral hematology-oncology ward in Iran. All patients admitted to hematology-oncology ward of Dr. Shariati Hospital during a 6-month period and received at least two anti-cancer or non-anti-cancer medications simultaneously were included. All being scheduled anti-cancer and non-anti-cancer medications both prescribed and administered during ward stay were considered for drug-drug interaction screening by Lexi-Interact On-Desktop software. One hundred and eighty-five drug-drug interactions with moderate or major severity were detected from 83 patients. Most of drug-drug interactions (69.73 %) were classified as pharmacokinetics. Fluconazole (25.95 %) was the most commonly offending medication in drug-drug interactions. Interaction of sulfamethoxazole-trimethoprim with fluconazole was the most common drug-drug interaction (27.27 %). Vincristine with imatinib was the only identified interaction between two anti-cancer agents. The number of administered medications during ward stay was considered as an independent risk factor for developing a drug-drug interaction. Potential moderate or major drug-drug interactions occur frequently in patients with hematological malignancies or related diseases. Performing larger standard studies are required to assess the real clinical and economical effects of drug-drug interactions on patients with hematological and non-hematological malignancies.

Drug-Drug Interactions in the Hematology and Oncology Department: A Real-Life Assessment of Frequency and Severity

Abstract 4250 Introduction:Drug-drug interactions (DDI) may lead to adverse drug events and constitute a risk for patients and a major challenge for treating physicians. Especially cancer patients receive a considerable number of concomitant drugs for the treatment of the underlying disease, the side effects of cancer therapy and due to preexisting comorbidities. As more and more older and severely compromised patients are treated today, polypharmacy and its related problems are of growing importance. However, data on the frequency and nature of DDI in cancer patients during hospitalization are rare. Methods:To determine the prevalence and nature of potential DDI, we performed a detailed chart review of all hospitalized patients in our hematology and oncology department during two randomly appointed days. All medications administered on these very days were extracted from the electronically archived patients' medical charts. DDI were identified and rated for severity and level of evidence using two different electronic interaction databases, Micromedex 2.0 and Lexi-Interact. Only DDI of at least moderate severity (i.e. rated as moderate, major or contraindicated) were included in the analysis, and documented irrespective of their actual clinical consequences. At the time of analysis, no routinely performed DDI screening or electronic DDI alert system was in place in our department. Results:Medication profiles from 157 inpatients were analyzed. Patients’ median age was 62 years (range: 25–94). Underlying diseases were leukemia (n=51), lymphoma (n=49), solid tumors (n=49) and non-malignant diseases (n=8). We evaluated a total of 1748 drug prescriptions with a median of 11 drugs per patient and day (range: 1–22). The median number of drugs was 9 in patients with solid tumors, 11 in patients with leukemia, and 12 in patients with lymphoma or non-malignant diseases. We found a total of 1153 drug interactions rated as of at least moderate severity by either Micromedex or Lexi-Interact, which corresponds to 734 DDI/100 patient days. The drug classes most frequently involved comprised antibiotics (23%), antifungals (21%), diuretics (20%), corticosteroids (17%), calcium channel blockers (11%), immunosuppressants (10%), benzodiazepines (9%) and opioid analgesics (8%). Antineoplastic drugs constituted 2.7% of all prescribed drugs, and were involved in 47 DDI (4%). The severity rating for all DDI was contraindicated, major and moderate in 2%, 24% and 74% of cases, respectively, according to the respective higher rating of both databases. The median number of DDI per patient was 5 (range: 0–31), and was highest for patients with leukemia with a median number of 7. 85% of patients experienced at least one DDI, and 26% of patients were exposed to ≥10 DDI on the evaluated day. The level of evidence of the 1153 DDI identified was excellent, good, fair and poor in 10%, 45%, 44% and 1% of cases, respectively.To identify the most clinically relevant DDI, we also determined the frequency and nature of those DDI which were rated as major severity or contraindicated by both databases. 61 DDI fulfilled these criteria and occurred in 33/157 patients (21%). The level of evidence of those 61 DDI was excellent, good and fair in 18%, 28% and 54% of cases, respectively. The most frequently detected potential clinical consequences of the 61 DDI rated as major or contraindicated by both databases were QT-prolongation with a variety of drug combinations (n=36), extrapyramidal reactions of different combinations including metoclopramide (n=5), and cyclosporine toxicity in combination with voriconazole (n=4). Conclusions:This retrospective DDI analysis revealed a high percentage of patients encountering DDI, including contraindicated drug combinations. Although some of these DDI are difficult to avoid or well manageable through close monitoring of the patients' clinical condition or laboratory results, there is a strong need for rising awareness of DDI to avoid patient harm. This is of particular relevance in the hematology and oncology setting, where numerous drugs are being applied, including cytotoxic agents. By identifying the most relevant DDI in this setting, these results allow targeted physician education and highlight the need to implement an electronic prescription system with DDI alerts in close cooperation with clinical pharmacists. Disclosures:No relevant conflicts of interest to declare.

Inhibitory Metabolic Drug Interactions with Newer Psychotropic Drugs: Inclusion in Package Inserts and Influences of Concurrence in Drug Interaction Screening Software

Food and Drug Administration (FDA) regulations mandate that package inserts (PIs) include observed or predicted clinically significant drug-drug interactions (DDIs), as well as the results of pharmacokinetic studies that establish the absence of effect. To quantify how frequently observed metabolic inhibition DDIs affecting US-marketed psychotropics are present in FDA-approved PIs and what influence the source of DDI information has on agreement between 3 DDI screening programs. The scientific literature and PIs were reviewed to determine all drug pairs for which there was rigorous evidence of a metabolic inhibition interaction or noninteraction. The DDIs were tabulated noting the source of evidence and the strength of agreement over chance. Descriptive statistics were used to examine the influence of source of DDI information on agreement among 3 DDI screening tools. Logistic regression was used to assess the influence of drug class, indication, generic status, regulatory approval date, and magnitude of effect on agreement between the literature and PI as well as agreement among the DDI screening tools. Thirty percent (13/44) of the metabolic inhibition DDIs affecting newer psychotropics were not mentioned in PIs. Drug class, indication, regulatory approval date, generic status, or magnitude of effect did not appear to be associated with more complete DDI information in PIs. DDIs found exclusively in PIs were 3.25 times more likely to be agreed upon by all 3 DDI screening tools than were those found exclusively in the literature. Generic status was inversely associated with agreement among the DDI screening tools (odds ratio 0.11; 95% CI 0.01 to 0.89). The presence in PIs of DDI information for newer psychotropics appears to have a strong influence on agreement among DDI screening tools. Users of DDI screening software should consult more than 1 source when considering interactions involving generic psychotropics.

Evaluation of drug–drug interaction screening software combined with pharmacist intervention

Drug-drug interactions (DDI) in hospitalized patients are highly prevalent and an important source of adverse drug reactions. DI computerized screening system can prevent the occurrence of some of these events. To evaluate the impact of drug-drug interaction (DDI) screening software combined with active intervention in preventing drug interactions. The study was conducted at General Hospital of Vitória da Conquista (HGVC), Brazil. A quasi-experimental study was used to evaluate the impact of IM-Pharma, a locally developed drug-drug interaction screening system, coupled with pharmacist intervention on adverse drug events in the hospital setting. The proportion of patients co-prescribed two interacting drugs were measured in two phases, prior the implementation of IM-Pharma and during the intervention period. DDI rates per 100 patient days were calculated before and after implementation. Risk ratios were estimated by Poisson regression models. A total of 6,834 instances of drug-drug interactions were identified; there was an average of 3.3 DDIs per patient in phase one and 2.5 in phase two, a reduction of 24 % (P = 0.03). There was a 71 % reduction in high-severity drug-drug interaction (P < 0.01). The risk for all DDIs decreased 50 % after the implementation of IM-Pharma (P < 0.01), and for those with high-severity, the reduction was 81 % (P < 0.01). The performance of IM-Pharma combined with pharmacist intervention was positive with an expressive reduction in the risk of DDIs.

The Need for Translational Research on Drug–Drug Interactions

Drug-drug interactions (DDIs) are an important clinical and public health concern. Although DDI screening now occurs during drug development, it is difficult to predict clinical importance based on in vitro experiments. Furthermore, older drugs that were not screened may have interactions that have not yet been identified. In this Commentary, we review the importance of DDIs and argue that a translational research approach is needed to produce clinically actionable information as well as generalizable biological knowledge.

Enhanced bile canaliculi formation enabling direct recovery of biliary metabolites of hepatocytes in 3D collagen gel microcavities

Analysis of biliary metabolites is essential to predict pharmacokinetics and hepatotoxicity during drug development. In this paper, we present a hepatocyte culture configuration that enables the direct recovery of bile acid that accumulates in bile canaliculi by embedding the hepatocytes in a 3D micropatterned collagen gel substrate. We investigated the formation of bile canaliculi in hepatocytes embedded in circular microcavities of various sizes and made from collagen gel. Image analyses using fluorescently labeled bile acid revealed that the area of bile canaliculi in embedded hepatocytes in a microcavity of 60 or 80 μm in diameter was enlarged when compared with other sized microcavities and those of hepatocytes cultured using conventional hepatocyte sandwich cultures. We successfully recovered bile acid from the enlarged bile canaliculi of hepatocytes cultured in microcavities using a glass capillary and quantified the amount recovered. Using our approach, the direct recovery of biliary metabolites, using hepatocyte cultures with enhanced biliary excretion and geometrically enlarged bile canaliculi, may enable accurate screening of pharmacokinetics and drug-drug interactions against drug transporters.

Hepatic Metabolism of 17α-hydroxyprogesterone Caproate in Mice,Rats, Dogs, Pigs, Rabbits and Baboons: A Comparison with Human Liver Microsomes and Human Hepatocytes

Hydroxyprogesterone caproate (17-OHPC) is used to prevent preterm labor in pregnant subjects. In the present study inter-species difference in the metabolism of 17-OHPC was evaluated using liver microsomes from mouse, rat, rabbit, pig, dog and baboon and compared with human liver microsomes and primary human hepatocyte cultures. In all the species evaluated, monohydroxylated 17-OHPC was observed to be the major metabolite. Significant inter-species difference in the rate of 17-OHPC metabolism was observed both qualitatively and quantitatively. Ketoconazole inhibited 17-OHPC metabolism in all the species evaluated confirming the dominant role of CYP3A (rats, rabbits, dog, pig, dog, baboon) and cyp3a (mice) in the metabolic pathway. It is concluded that monohydroxylated 17-OHPC is the major metabolite of 17-OHPC in various animal species. The baboon seems to provide the most suitable model for evaluating the pharmacokinetics of 17-OHPC in animals and screening for potential drug-drug interactions although interspecies variation exists in the disposition of 17-OHPC.

Evaluation of a wireless handheld medication management device in the prevention of drug-drug interactions in a Medicaid population.

With the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act, widespread adoption of certain health information technologies, such as electronic health records (EHRs) and electronic prescribing (e-prescribing), is imminent. Drug-drug interaction (DDI) screening and medication history information are commonly incorporated into health information exchange systems to improve medical decision making, safety, and quality of care, but the value of these features is unclear. To evaluate the effect of providing access to an early generation electronic medication management program with medication history accessible to prescribers via a wireless handheld personal digital assistant (PDA) device on the incidence of potential DDIs (i.e., DDIs that may or may not cause patient harm). This study employed a retrospective pre-intervention/post-intervention study design with a comparison group to evaluate the effectiveness of a wireless handheld medication management program in preventing serious potential DDIs. Licensed prescribers in a state Medicaid program who wrote prescriptions during the period from August 2003 through June 2006 were included in this study. The intervention (PDA) group consisted of clinicians who requested and were granted access to the wireless handheld device containing prescription drug history between August 1, 2004, and June 30, 2005. Initially the device contained 100-day patient-specific medication history, but other functionalities were added during the study period including the ability to check for drug-drug interactions and e-prescribing. The comparison group consisted of prescribers who sent a request to obtain, but did not receive, the wireless handheld device during the same time period. Baseline prescribing patterns of 25 previously identified clinically important potential DDIs were assessed over two 12-month periods, one period prior to (baseline) and one period after (follow-up) an index date (date of device deployment for PDA group; date of request for comparison group). A random-effects negative binomial model was used to analyze the primary outcome, the number of potential DDIs per prescriber per 12-month time period. A secondary outcome of interest, the likelihood that a prescriber would prescribe at least 1 potentially interacting medication pair during the baseline and follow-up periods, was analyzed using a random-effects logistic model. A total of 1,615 prescribers constituted the PDA group, and 600 prescribers made up the comparison group. Prescribers in the 2 groups were significantly different in their specialty practice areas (P less than 0.001), number of pharmacy claims at baseline (P less than 0.001), and the likelihood of prescribing at least 1 potential DDI combination during the 1-year baseline period (P=0.003). However, the prescriber groups were similar in their average age (P=0.241) and geographic location (P=0.181). The most widely prescribed potential DDIs included those involving warfarin with nonsteroidal anti-inflammatory drugs (NSAIDs) and thyroid hormones. The median number of patient medication history updates requested per PDA group prescriber during follow-up was 24 (range 0 to 1,073). At baseline, 1,104 (68.4%) of the PDA group and 449 (74.8%) of the comparison group had no potential DDIs. During the next year, 1,131 (70.0%) and 462 (77.0%) of the PDA group and comparison group, respectively, had no DDIs. The incidence rate ratio was 1.01 (95% CI=0.87-1.17) for the PDA group relative to the comparison group for change in number of potential DDIs. In the logistic regression model, the odds of prescribing at least 1 potential DDI did not significantly differ by group (odds ratio=1.26, 95% CI=0.96-1.66). These results indicate that there was no significant difference between the intervention and comparison group with regard to the change in the rate of potential DDIs between the baseline and follow-up periods. A stand-alone medication management program in a wireless PDA device was not frequently used by most prescribers to update patient medication histories and was not associated with a reduction in the rate of prescribing potentially clinically important DDIs.

Contradictions in Contraindications for Drug-Drug Interactions

Health-care providers use computerized provider order entry as a component of the electronic medical record system in conjunction with clinical decision support. An important aspect of clinical decision support is screening of drug-drug interactions including contraindications in the labeling approved by the Food and Drug Administration. Currently, there are inconsistencies between the knowledge databases and the official package labeling for contraindications on drug-drug interactions. Unnecessary warnings can cause alert fatigue among clinicians and potentially influence adoption of true contraindications for drug prescribing. Each institution should evaluate the accuracy and completeness of the knowledge databases used for screening of contraindications on drug-drug interactions.