Drug Burden Index and Its Association With Hip Fracture Among Older Adults: A National Population-Based Study.

BACKGROUND: The Drug Burden Index (DBI) calculates the total sedative and anticholinergic load of prescribed medications and is associated with functional decline and hip fractures in older adults. However, it is unknown if confounding factors influence the relationship between the DBI and hip fractures. The objective of this study was to evaluate the association between the DBI and hip fractures, after correcting for mortality and multiple potential confounding factors. METHODS: A competing-risks regression analysis conducted on a prospectively recruited New Zealand community-dwelling older population who had a standardized (International Resident Assessment Instrument) assessment between September 1, 2012, and October 31, 2015, the study's end date. Outcome measures were survival status and hip fracture, with time-varying DBI exposure derived from 90-day time intervals. The multivariable competing-risks regression model was adjusted for a large number of medical comorbidities and activities of daily living. RESULTS: Among 70,553 adults assessed, 2,249 (3.2%) experienced at least one hip fracture, 20,194 (28.6%) died without experiencing a fracture, and 48,110 (68.2%) survived without a fracture. The mean follow-up time was 14.9 months (range: 1 day, 37.9 months). The overall DBI distribution was highly skewed, with median time-varying DBI exposure ranging from 0.93 (Q1 = 0.0, Q3 = 1.84) to 0.96 (Q1 = 0.0, Q3 = 1.90). DBI was significantly related to fracture incidence in unadjusted (p < .001) and adjusted (p < .001) analyses. The estimated subhazard ratio was 1.52 (95% confidence interval: 1.28-1.81) for those with DBI > 3 compared with those with DBI = 0 in the adjusted analysis. CONCLUSIONS: In this study, increasing DBI was associated with a higher likelihood of fractures after accounting for the competing risk of mortality and adjusting for confounders. The results of this unique study are important in validating the DBI as a guide for medication management and it could help reduce the risk of hip fractures in older adults.

Pharmacotherapy in Older Adults with Cardiovascular Disease: Report from an American College of Cardiology, American Geriatrics Society, and National Institute on Aging Workshop.

OBJECTIVES: To identify the top priority areas for research to optimize pharmacotherapy in older adults with cardiovascular disease (CVD). DESIGN: Consensus meeting. SETTING: Multidisciplinary workshop supported by the National Institute on Aging, the American College of Cardiology, and the American Geriatrics Society, February 6-7, 2017. PARTICIPANTS: Leaders in the Cardiology and Geriatrics communities, (officers in professional societies, journal editors, clinical trialists, Division chiefs), representatives from the NIA; National Heart, Lung, and Blood Institute; Food and Drug Administration; Centers for Medicare and Medicaid Services, Alliance for Academic Internal Medicine, Patient-Centered Outcomes Research Institute, Agency for Healthcare Research and Quality, pharmaceutical industry, and trainees and early career faculty with interests in geriatric cardiology. MEASUREMENTS: Summary of workshop proceedings and recommendations. RESULTS: To better align older adults' healthcare preferences with their care, research is needed to improve skills in patient engagement and communication. Similarly, to coordinate and meet the needs of older adults with multiple comorbidities encountering multiple healthcare providers and systems, systems and disciplines must be integrated. The lack of data from efficacy trials of CVD medications relevant to the majority of older adults creates uncertainty in determining the risks and benefits of many CVD therapies; thus, developing evidence-based guidelines for older adults with CVD is a top research priority. Polypharmacy and medication nonadherence lead to poor outcomes in older people, making research on appropriate prescribing and deprescribing to reduce polypharmacy and methods to improve adherence to beneficial therapies a priority. CONCLUSION: The needs and circumstances of older adults with CVD differ from those that the current medical system has been designed to meet. Optimizing pharmacotherapy in older adults will require new data from traditional and pragmatic research to determine optimal CVD therapy, reduce polypharmacy, increase adherence, and meet person-centered goals. Better integration of the multiple systems and disciplines involved in the care of older adults will be essential to implement and disseminate best practices. J Am Geriatr Soc 67:371-380, 2019.

ß2-adrenoceptor agonists and antagonists and risk of Parkinson's disease.

BACKGROUND: ß2-adrenoreceptors have recently been identified as regulators of the α-synuclein gene, which is implicated in the pathogenesis of Parkinson's disease. OBJECTIVE: The objectives of this study were to assess the association between use of ß2-agonists and ß-antagonists and the risk of developing PD. METHODS: We conducted a nested case-control study in a cohort of 1,762,164 adults without a diagnosis of PD. They were identified on January, 1, 2004, from the electronic medical records of the largest health care provider in Israel. Participants were followed up until June 30, 2017, for the occurrence of PD. Ten randomly selected controls were matched to each case of PD on age, sex, ethnic group, and duration of follow-up. RESULTS: During follow-up 11,314 patients were newly diagnosed with PD and were matched with 113,140 controls. An increased risk of PD was seen with the use of nonselective ß-antagonists (RR, 2.04 [1.90-2.20]) but not with the use of selective ß1-antagonists (RR, 1.00 [0.95-1.05]). Use of ß2-agonists was associated with reduced risk of PD (RR, 0.89 [0.82-0.96] for short-acting; RR, 0.84 [0.76-0.93] for long-acting; and RR, 0.49 [0.25-0.92] for ultra-long-acting ß2-agonists). In an analysis of individual drugs, propranolol and salbutamol were significantly associated with PD risk, even when these drugs were ascertained 5 years prior to the index date, compared with nonusers (RR, 1.31 [1.08-1.58] and 1.89 {1.53-2.33]) in patients who filled <6 and ≥6 propranolol prescriptions, respectively; the corresponding RRs for salbutamol were 0.95 (0.83-1.08) and 0.65 (0.45-0.94), respectively. CONCLUSIONS: Use of propranolol appears to be associated with an increased risk of PD, whereas use of ß2-agonists is associated with a decreased risk of PD. © 2018 International Parkinson and Movement Disorder Society.

Drug Burden and its Association with Falls Among Older Adults in New Zealand: A National Population Cross-Sectional Study.

BACKGROUND: Adverse outcomes associated with advanced diseases are often exacerbated by polypharmacy. OBJECTIVES: The current study investigated an association between exposure to anticholinergic and sedative medicines and falls in community-dwelling older people, after controlling for potential confounders. METHODS: We conducted a retrospective cross-sectional study of a continuously recruited national cohort of community-dwelling New Zealanders aged 65 years and over. Participants had an International Resident Assessment Instrument-Home Care (interRAI-HC) assessment between 1 September 2012 and 31 January 2016. InterRAI-HC is a comprehensive, multi-domain, standardised assessment. This study captured 18 variables, including fall frequency, from the interRAI. These data were deterministically matched with the Drug Burden Index (DBI) for each participant, derived from an anonymised national dispensed pharmaceuticals database. DBI groupings were statistically ascertained, and ordinal regression models employed. RESULTS: Overall, there were 71,856 participants, with a mean age of 82.7 years (range 65-106); 43,802 (61.0%) were female, and 63,578 (88.5%) were New Zealand European. In unadjusted and adjusted analyses, DBI groupings were related to falls (p < 0.001). A DBI score > 3 was associated with a 41% increase in falls compared with a DBI score of 0 (p < 0.001). There was a 'dose-response' relationship between DBI levels and falls risk. CONCLUSIONS: DBI was found to be independently and positively associated with a greater risk of falls in this cohort after adjustment for 18 known confounders. We suggest that the DBI could be a valuable tool for clinicians to use alongside electronic prescribing to help reduce falls in older people.

Use of Biomedical Ontologies for Integration of Biological Knowledge for Learning and Prediction of Adverse Drug Reactions.

Drug-induced toxicity is a major public health concern that leads to patient morbidity and mortality. To address this problem, the Food and Drug Administration is working on the PredicTox initiative, a pilot research program on tyrosine kinase inhibitors, to build mechanistic and predictive models for drug-induced toxicity. This program involves integrating data acquired during preclinical studies and clinical trials within pharmaceutical company development programs that they have agreed to put in the public domain and in publicly available biological, pharmacological, and chemical databases. The integration process is accommodated by biomedical ontologies, a set of standardized vocabularies that define terms and logical relationships between them in each vocabulary. We describe a few programs that have used ontologies to address biomedical questions. The PredicTox effort is leveraging the experience gathered from these early initiatives to develop an infrastructure that allows evaluation of the hypothesis that having a mechanistic understanding underlying adverse drug reactions will improve the capacity to understand drug-induced clinical adverse drug reactions.

In Vitro-In Vivo Extrapolation of Metabolism- and Transporter-Mediated Drug-Drug Interactions-Overview of Basic Prediction Methods.

Evaluation of drug-drug interaction (DDI) risk is vital to establish benefit-risk profiles of investigational new drugs during drug development. In vitro experiments are routinely conducted as an important first step to assess metabolism- and transporter-mediated DDI potential of investigational new drugs. Results from these experiments are interpreted, often with the aid of in vitro-in vivo extrapolation methods, to determine whether and how DDI should be evaluated clinically to provide the basis for proper DDI management strategies, including dosing recommendations, alternative therapies, or contraindications under various DDI scenarios and in different patient population. This article provides an overview of currently available in vitro experimental systems and basic in vitro-in vivo extrapolation methodologies for metabolism- and transporter-mediated DDIs.

Linking MedDRA(®)-Coded Clinical Phenotypes to Biological Mechanisms by the Ontology of Adverse Events: A Pilot Study on Tyrosine Kinase Inhibitors.

INTRODUCTION: A translational bioinformatics challenge exists in connecting population and individual clinical phenotypes in various formats to biological mechanisms. The Medical Dictionary for Regulatory Activities (MedDRA(®)) is the default dictionary for adverse event (AE) reporting in the US Food and Drug Administration Adverse Event Reporting System (FAERS). The ontology of adverse events (OAE) represents AEs as pathological processes occurring after drug exposures. OBJECTIVES: The aim of this work was to establish a semantic framework to link biological mechanisms to phenotypes of AEs by combining OAE with MedDRA(®) in FAERS data analysis. We investigated the AEs associated with tyrosine kinase inhibitors (TKIs) and monoclonal antibodies (mAbs) targeting tyrosine kinases. The five selected TKIs/mAbs (i.e., dasatinib, imatinib, lapatinib, cetuximab, and trastuzumab) are known to induce impaired ventricular function (non-QT) cardiotoxicity. RESULTS: Statistical analysis of FAERS data identified 1053 distinct MedDRA(®) terms significantly associated with TKIs/mAbs, where 884 did not have corresponding OAE terms. We manually annotated these terms, added them to OAE by the standard OAE development strategy, and mapped them to MedDRA(®). The data integration to provide insights into molecular mechanisms of drug-associated AEs was performed by including linkages in OAE for all related AE terms to MedDRA(®) and the existing ontologies, including the human phenotype ontology (HP), Uber anatomy ontology (UBERON), and gene ontology (GO). Sixteen AEs were shared by all five TKIs/mAbs, and each of 17 cardiotoxicity AEs was associated with at least one TKI/mAb. As an example, we analyzed "cardiac failure" using the relations established in OAE with other ontologies and demonstrated that one of the biological processes associated with cardiac failure maps to the genes associated with heart contraction. CONCLUSION: By expanding the existing OAE ontological design, our TKI use case demonstrated that the combination of OAE and MedDRA(®) provides a semantic framework to link clinical phenotypes of adverse drug events to biological mechanisms.

Bidirectional interactions between indomethacin and the murine intestinal microbiota.

The vertebrate gut microbiota have been implicated in the metabolism of xenobiotic compounds, motivating studies of microbe-driven metabolism of clinically important drugs. Here, we studied interactions between the microbiota and indomethacin, a nonsteroidal anti-inflammatory drug (NSAID) that inhibits cyclooxygenases (COX) -1 and -2. Indomethacin was tested in both acute and chronic exposure models in mice at clinically relevant doses, which suppressed production of COX-1- and COX-2-derived prostaglandins and caused small intestinal (SI) damage. Deep sequencing analysis showed that indomethacin exposure was associated with alterations in the structure of the intestinal microbiota in both dosing models. Perturbation of the intestinal microbiome by antibiotic treatment altered indomethacin pharmacokinetics and pharmacodynamics, which is probably the result of reduced bacterial ß-glucuronidase activity. Humans show considerable inter-individual differences in their microbiota and their responses to indomethacin - thus, the drug-microbe interactions described here provide candidate mediators of individualized drug responses.

Replication - why we need to publish our findings.

The pharmacology research sector is changing to accommodate a need for greater transparency and better standards. The themed articles contained herein explain how Pharmacology Research and Perspectives (PR&P) has responded to this agenda. This issue of PR&P contains three articles that consider the reliability of pharmacological research publications, and approaches to their improvement in this regard. This first article explains the importance of publishing findings that confirm or repudiate published findings (so called "replication" studies). It also emphasizes that PR&P actively encourages submission of such articles, and seeks to oppose the publication bias that favors publication of "positive" findings. The second paper explores some initiatives to publish "negative" clinical findings, including a PR&P initiative. The final paper elaborates a toolkit that can be applied to drug discovery research to facilitate the reliability of findings.

Logic-Based and Cellular Pharmacodynamic Modeling of Bortezomib Responses in U266 Human Myeloma Cells.

Systems models of biological networks show promise for informing drug target selection/qualification, identifying lead compounds and factors regulating disease progression, rationalizing combinatorial regimens, and explaining sources of intersubject variability and adverse drug reactions. However, most models of biological systems are qualitative and are not easily coupled with dynamical models of drug exposure-response relationships. In this proof-of-concept study, logic-based modeling of signal transduction pathways in U266 multiple myeloma (MM) cells is used to guide the development of a simple dynamical model linking bortezomib exposure to cellular outcomes. Bortezomib is a commonly used first-line agent in MM treatment; however, knowledge of the signal transduction pathways regulating bortezomib-mediated cell cytotoxicity is incomplete. A Boolean network model of 66 nodes was constructed that includes major survival and apoptotic pathways and was updated using responses to several chemical probes. Simulated responses to bortezomib were in good agreement with experimental data, and a reduction algorithm was used to identify key signaling proteins. Bortezomib-mediated apoptosis was not associated with suppression of nuclear factor κB (NFκB) protein inhibition in this cell line, which contradicts a major hypothesis of bortezomib pharmacodynamics. A pharmacodynamic model was developed that included three critical proteins (phospho-NFκB, BclxL, and cleaved poly (ADP ribose) polymerase). Model-fitted protein dynamics and cell proliferation profiles agreed with experimental data, and the model-predicted IC50 (3.5 nM) is comparable to the experimental value (1.5 nM). The cell-based pharmacodynamic model successfully links bortezomib exposure to MM cellular proliferation via protein dynamics, and this model may show utility in exploring bortezomib-based combination regimens.

Hypothyroidism is a Risk Factor for New-Onset Diabetes: A Cohort Study.

OBJECTIVE: To identify risk factors for the development of statin-associated diabetes mellitus (DM). RESEARCH DESIGN AND METHODS: The study was conducted in two phases. Phase one involved high-throughput in silico processing of a large amount of biomedical data to identify risk factors for the development of statin-associated DM. In phase two, the most prominent risk factor identified was confirmed in an observational cohort study at Clalit, the largest health care organization in Israel. Time-dependent Poisson regression multivariable models were performed to assess rate ratios (RRs) with 95% CIs for DM occurrence. RESULTS: A total of 39,263 statin nonusers were matched by propensity score to 20,334 highly compliant statin initiators in 2004-2005 and followed until the end of 2010. Within 59,597 statin users and nonusers in a multivariable model, hypothyroidism and subclinical hypothyroidism carried an increased risk for DM (RR 1.53 [95% CI 1.31-1.79] and 1.75 [1.40-2.18], respectively). Hypothyroidism increased DM risk irrespective of statin treatment (RR 2.06 [1.42-2.99] and 1.66 [1.05-2.64] in statin users and nonusers, respectively). Subclinical hypothyroidism risk for DM was prominent only upon statin use (RR 1.94 [1.13-3.34] and 1.20 [0.52-2.75] in statin users and nonusers, respectively). Patients with hypothyroidism treated with thyroid hormone replacement therapy were not at increased risk for DM. CONCLUSIONS: Hypothyroidism is a risk factor for DM. Subclinical hypothyroidism-associated risk for DM is prominent only upon statin use. Identifying and treating hypothyroidism and subclinical hypothyroidism might reduce DM risk. Future clinical studies are needed to confirm the findings.

Consensus recommendations for systematic evaluation of drug-drug interaction evidence for clinical decision support.

BACKGROUND: Healthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug-drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations. OBJECTIVE: The aim of this study was to provide recommendations for systematic evaluation of evidence for DDIs from the scientific literature, drug product labeling, and regulatory documents. METHODS: A conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 18 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar 12 times from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations. RESULTS: We developed expert consensus answers to the following three key questions. (i) What is the best approach to evaluate DDI evidence? (ii) What evidence is required for a DDI to be applicable to an entire class of drugs? (iii) How should a structured evaluation process be vetted and validated? CONCLUSION: Evidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug compendia and clinical decision support systems in which these recommendations are implemented should be able to provide higher-quality information about DDIs.

Adverse Event Detection and Labeling in Pediatric Drug Development: Antiretroviral Drugs.

BACKGROUND: Pediatric safety studies are conducted for drugs undergoing development for use in pediatric patients. The objective of this study was to describe safety studies and compare adverse events of antiretroviral drugs between pediatric patients and adult subjects. METHODS: Pediatric and adult adverse event data were obtained from US Food and Drug Administration (FDA)-approved drug labels for 9 antiretroviral drugs with pediatric indications approved by the FDA prior to 2013. For adverse events (AEs) reported in both pediatric patients and adult subjects, the risk difference (RD) and associated confidence interval (CI) were calculated. RESULTS: Of 35 drug-AE combinations, 10 AEs were reported at statistically significantly ( P < .05) higher incidence rates in the pediatric population than in the adult population, and 3 AEs were reported at statistically significantly higher rates in the adult population than in the pediatric population. The largest differences where the risk of an AE was greater in pediatric patients than in adult subjects were for rash with efavirenz (RD = 36.24% [95% CI, 21.1 to 50.53]), diarrhea with efavirenz (RD = 24.53% [95% CI, 9.06 to 39.57]), and rash with nevirapine (RD = 16.14% [95% CI, 9.7 to 24.27]). The largest differences where the risk of an adverse event was lower in pediatric patients than in adult subjects were for headache with abacavir (RD = -12% [95% CI, -16.81 to -6.89]), diarrhea with tipranavir (RD = -11.32% [95% CI, -15.02 to -5.6]), and diarrhea with lamivudine (RD = -9.88% [95% CI, -15.91 to -3.98]). CONCLUSIONS: The adult adverse event experience provides preliminary data for pediatric drug safety, yet the specific types of adverse effects and frequencies may not be predicted in children based exclusively on adults. As adult safety data do not fully inform the pediatric safety profile, pediatric safety studies should continue to be conducted separately for drugs undergoing testing in pediatric patients.

The utility of modeling and simulation in drug development and regulatory review.

US Food and Drug Administration (FDA) has identified innovation in clinical evaluations as a major scientific priority area. This paper provides case studies and updates to describe the efforts by the FDA's Office of Clinical Pharmacology in its development and application of regulatory science, focusing on modeling and simulation. Key issues and challenges are identified that need to be addressed to promote the uptake of modeling and simulation approaches in drug regulation. Published 2013. This article is a U.S. Government work and is in the public domain in the USA. 102:2912-2923, 2013.