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Co-prescription trends in a large cohort of subjects predict substantial drug-drug interactions.

ABSTRACT: Pharmaceutical prescribing and drug-drug interaction data underlie recommendations on drug combinations that should be avoided or closely monitored by prescribers. Because the number of patients taking multiple medications is increasing, a comprehensive view of prescribing patterns in patients is important to better assess real world pharmaceutical response and evaluate the potential for multi-drug interactions. We obtained self-reported prescription data from NHANES surveys between 1999 and 2010, and confirm the previously reported finding of increasing drug use in the elderly. We studied co-prescription drug trends by focusing on the 2009-2010 survey, which contains prescription data on 690 drugs used by 10,537 subjects. We found that medication profiles were unique for individuals aged 65 years or more, with ?98 unique drug regimens encountered per 100 subjects taking 3 or more medications. When drugs were viewed by therapeutic class, it was found that the most commonly prescribed drugs were not the most commonly co-prescribed drugs for any of the 16 drug classes investigated. We cross-referenced these medication lists with drug interaction data from Drugs.com to evaluate the potential for drug interactions. The number of drug alerts rose proportionally with the number of co-prescribed medications, rising from 3.3 alerts for individuals prescribed 5 medications to 11.7 alerts for individuals prescribed 10 medications. We found 22% of elderly subjects taking both a substrate and inhibitor of a given cytochrome P450 enzyme, and 4% taking multiple inhibitors of the same enzyme simultaneously. By examining drug pairs prescribed in 0.1% of the population or more, we found low agreement between co-prescription rate and co-discussion in the literature. These data show that prescribing trends in treatment could drive a large extent of individual variability in drug response, and that current pairwise approaches to assessing drug-drug interactions may be inadequate for predicting real world outcomes.

SUBMITTER: Sutherland JJ

PROVIDER: S-EPMC4349653 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Co-prescription trends in a large cohort of subjects predict substantial drug-drug interactions.

Sutherland Jeffrey J JJ Daly Thomas M TM Liu Xiong X Goldstein Keith K Johnston Joseph A JA Ryan Timothy P TP

PloS one 20150304 3

Pharmaceutical prescribing and drug-drug interaction data underlie recommendations on drug combinations that should be avoided or closely monitored by prescribers. Because the number of patients taking multiple medications is increasing, a comprehensive view of prescribing patterns in patients is important to better assess real world pharmaceutical response and evaluate the potential for multi-drug interactions. We obtained self-reported prescription data from NHANES surveys between 1999 and 201 ...[more]

PMID: 25739022

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Project description:BackgroundDrug-induced QT-interval prolongation is associated with occurrence of potentially fatal Torsades de Pointes arrhythmias (TdP). So far, data regarding the overall burden of QT-interval prolonging drugs (QT-drugs) in geriatric patients are limited.ObjectiveThis study was performed to assess the individual burden of QT-interval prolonging drugs (QT-drugs) in geriatric polymedicated patients and to identify the most frequent and risky combinations of QT-drugs.MethodsIn the discharge medication of geriatric patients between July 2009 and June 2013 from the Geriatrics in Bavaria-Database (GiB-DAT) (co)-prescriptions of QT-drugs were investigated. QT-drugs were classified according to a publicly available reference site (CredibleMeds®) as ALL-QT-drugs (associated with any QT-risk) or High-risk-QT-drugs (corresponding to QT-drugs with known risk of Torsades de Pointes according to CredibleMeds®) and in addition as SmPC-high-risk-QT-drugs (according to the German prescribing information (SmPC) contraindicated co-prescription with other QT-drugs).ResultsOf a cohort of 130,434 geriatric patients (mean age 81 years, 67% women), prescribed a median of 8 drugs, 76,594 patients (58.7%) received at least one ALL-QT-drug. Co-prescriptions of two or more ALL-QT-drugs were observed in 28,768 (22.1%) patients. Particularly risky co-prescriptions of High-risk-QT-drugs or SmPC-high-risk-QT-drugs with at least on further QT-drug occurred in 55.9% (N = 12,633) and 54.2% (N = 12,429) of these patients, respectively. Consideration of SmPCs (SmPC-high-risk-QT-drugs) allowed the identification of an additional 15% (N = 3,999) patients taking a risky combination that was not covered by the commonly used CredibleMeds® classification. Only 20 drug-drug combinations accounted for more than 90% of these potentially most dangerous co-prescriptions.ConclusionIn a geriatric study population co-prescriptions of two and more QT-drugs were common. A considerable proportion of QT-drugs with higher risk only could be detected by using more than one classification-system. Local adaption of international classifications can improve identification of patients at risk.

Project description:Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.

Project description:PurposeTo report historical patterns of pharmaceutical expenditures, to identify factors that may influence future spending, and to predict growth in drug spending in 2021 in the United States, with a focus on the nonfederal hospital and clinic sectors.MethodsHistorical patterns were assessed by examining data on drug purchases from manufacturers using the IQVIA National Sales Perspectives database. Factors that may influence drug spending in hospitals and clinics in 2021 were reviewed-including new drug approvals, patent expirations, and potential new policies or legislation. Focused analyses were conducted for biosimilars, cancer drugs, generics, coronavirus disease 2019 (COVID-19) pandemic influence, and specialty drugs. For nonfederal hospitals, clinics, and overall (all sectors), estimates of growth of pharmaceutical expenditures in 2021 were based on a combination of quantitative analyses and expert opinion.ResultsIn 2020, overall pharmaceutical expenditures in the United States grew 4.9% compared to 2019, for a total of $535.3 billion. Utilization (a 2.9% increase) and new drugs (a 1.8% increase) drove this increase, with price changes having minimal influence (a 0.3% increase). Adalimumab was the top drug in 2020, followed by apixaban and insulin glargine. Drug expenditures were $35.3 billion (a 4.6% decrease) and $98.4 billion (an 8.1% increase) in nonfederal hospitals and clinics, respectively. In clinics, growth was driven by new products and increased utilization, whereas in hospitals the decrease in expenditures was driven by reduced utilization. Several new drugs that will influence spending are expected to be approved in 2021. Specialty and cancer drugs will continue to drive expenditures along with the evolution of the COVID-19 pandemic.ConclusionFor 2021, we expect overall prescription drug spending to rise by 4% to 6%, whereas in clinics and hospitals we anticipate increases of 7% to 9% and 3% to 5%, respectively, compared to 2020. These national estimates of future pharmaceutical expenditure growth may not be representative of any particular health system because of the myriad of local factors that influence actual spending.

Project description:ObjectiveTo assess the association between long term prescription opioid treatment medically dispensed for non-cancer pain and the initiation of injection drug use (IDU) among individuals without a history of substance use.DesignRetrospective cohort study.SettingLarge administrative data source (containing information for about 1.7 million individuals tested for hepatitis C virus or HIV in British Columbia, Canada) with linkage to administrative health databases, including dispensations from community pharmacies.ParticipantsIndividuals age 11-65 years and without a history of substance use (except alcohol) at baseline.Main outcome measuresEpisodes of prescription opioid use for non-cancer pain were identified based on drugs dispensed between 2000 and 2015. Episodes were classified by the increasing length and intensity of opioid use (acute (lasting <90 episode days), episodic (lasting ≥90 episode days; with <90 days' drug supply and/or <50% episode intensity), and chronic (lasting ≥90 episode days; with ≥90 days' drug supply and ≥50% episode intensity)). People with a chronic episode were matched 1:1:1:1 on socioeconomic variables to those with episodic or acute episodes and to those who were opioid naive. IDU initiation was identified by a validated administrative algorithm with high specificity. Cox models weighted by inverse probability of treatment weights assessed the association between opioid use category (chronic, episodic, acute, opioid naive) and IDU initiation.Results59 804 participants (14 951 people from each opioid use category) were included in the matched cohort, and followed for a median of 5.8 years. 1149 participants initiated IDU. Cumulative probability of IDU initiation at five years was highest for participants with chronic opioid use (4.0%), followed by those with episodic use (1.3%) and acute use (0.7%), and those who were opioid naive (0.4%). In the inverse probability of treatment weighted Cox model, risk of IDU initiation was 8.4 times higher for those with chronic opioid use versus those who were opioid naive (95% confidence interval 6.4 to 10.9). In a sensitivity analysis limited to individuals with a history of chronic pain, cumulative risk for those with chronic use (3.4% within five years) was lower than the primary results, but the relative risk was not (hazard ratio 9.7 (95% confidence interval 6.5 to 14.5)). IDU initiation was more frequent at higher opioid doses and younger ages.ConclusionsThe rate of IDU initiation among individuals who received chronic prescription opioid treatment for non-cancer pain was infrequent overall (3-4% within five years) but about eight times higher than among opioid naive individuals. These findings could have implications for strategies to prevent IDU initiation, but should not be used as a reason to support involuntary tapering or discontinuation of long term prescription opioid treatment.

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Co-prescription trends in a large cohort of subjects predict substantial drug-drug interactions.

Publications

Co-prescription trends in a large cohort of subjects predict substantial drug-drug interactions.

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