Predicting potential adverse events using safety data from marketed drugs.

BACKGROUND: While clinical trials are considered the gold standard for detecting adverse events, often these trials are not sufficiently powered to detect difficult to observe adverse events. We developed a preliminary approach to predict 135 adverse events using post-market safety data from marketed drugs. Adverse event information available from FDA product labels and scientific literature for drugs that have the same activity at one or more of the same targets, structural and target similarities, and the duration of post market experience were used as features for a classifier algorithm. The proposed method was studied using 54 drugs and a probabilistic approach of performance evaluation using bootstrapping with 10,000 iterations. RESULTS: Out of 135 adverse events, 53 had high probability of having high positive predictive value. Cross validation showed that 32% of the model-predicted safety label changes occurred within four to nine years of approval (median: six years). CONCLUSIONS: This approach predicts 53 serious adverse events with high positive predictive values where well-characterized target-event relationships exist. Adverse events with well-defined target-event associations were better predicted compared to adverse events that may be idiosyncratic or related to secondary target effects that were poorly captured. Further enhancement of this model with additional features, such as target prediction and drug binding data, may increase accuracy.

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.

Data Mining FAERS to Analyze Molecular Targets of Drugs Highly Associated with Stevens-Johnson Syndrome.

Drug features that are associated with Stevens-Johnson syndrome (SJS) have not been fully characterized. A molecular target analysis of the drugs associated with SJS in the FDA Adverse Event Reporting System (FAERS) may contribute to mechanistic insights into SJS pathophysiology. The publicly available version of FAERS was analyzed to identify disproportionality among the molecular targets, metabolizing enzymes, and transporters for drugs associated with SJS. The FAERS in-house version was also analyzed for an internal comparison of the drugs most highly associated with SJS. Cyclooxygenases 1 and 2, carbonic anhydrase 2, and sodium channel 2 alpha were identified as disproportionately associated with SJS. Cytochrome P450 (CYPs) 3A4 and 2C9 are disproportionately represented as metabolizing enzymes of the drugs associated with SJS adverse event reports. Multidrug resistance protein 1 (MRP-1), organic anion transporter 1 (OAT1), and PEPT2 were also identified and are highly associated with the transport of these drugs. A detailed review of the molecular targets identifies important roles for these targets in immune response. The association with CYP metabolizing enzymes suggests that reactive metabolites and oxidative stress may have a contributory role. Drug transporters may enhance intracellular tissue concentrations and also have vital physiologic roles that impact keratinocyte proliferation and survival. Data mining FAERS may be used to hypothesize mechanisms for adverse drug events by identifying molecular targets that are highly associated with drug-induced adverse events. The information gained may contribute to systems biology disease models.

Safety and efficacy of flumazenil for reversal of iatrogenic benzodiazepine-associated delirium toxicity during treatment of alcohol withdrawal, a retrospective review at one center.

Both alcohol withdrawal syndrome (AWS) and benzodiazepines can cause delirium. Benzodiazepine-associated delirium can complicate AWS and prolong hospitalization. Benzodiazepine delirium can be diagnosed with flumazenil, a GABA-A receptor antagonist. By reversing the effects of benzodiazepines, flumazenil is theorized to exacerbate symptoms of AWS and precludes its use. For patients being treated for alcohol withdrawal, flumazenil can diagnose and treat benzodiazepine delirium without precipitating serious or life-threatening adverse events. Hospital admission records were retrospectively reviewed for patients with the diagnosis of AWS who received both benzodiazepines and flumazenil from December 2006 to June 2012 at a university-affiliated inpatient toxicology center. The day of last alcohol consumption was estimated from available blood alcohol content or subjective history. Corresponding benzodiazepine, flumazenil, and adjunctive sedative pharmacy records were reviewed, as were demographic, clinical course, and outcome data. Eighty-five patients were identified (average age 50.3 years). Alcohol concentrations were detectable for 42 patients with average 261 mg/dL (10-530 mg/dL). Eighty patients were treated with adjunctive agents for alcohol withdrawal including antipsychotics (n = 57), opioids (n = 27), clonidine (n = 35), and phenobarbital (n = 23). Average time of flumazenil administration was 4.7 days (1-11 days) after abstinence, and average dose was 0.5 mg (0.2-1 mg). At the time of flumazenil administration, delirium was described as hypoactive (n = 21), hyperactive (n = 15), mixed (n = 41), or not specified (n = 8). Response was not documented in 11 cases. Sixty-two (72.9 %) patients had significant objective improvement after receiving flumazenil. Fifty-six patients required more than one dose (average 5.6 doses). There were no major adverse events and minor adverse effects included transiently increased anxiety in two patients: 1 patient who received 0.5 mg on abstinence day 2 and another patient who received 0.2 mg flumazenil on abstinence day 11. This is the largest series diagnosing benzodiazepine delirium after AWS in patients receiving flumazenil. During the treatment of AWS, if delirium is present on day 5, a test dose of flumazenil may be considered to establish benzodiazepine delirium. With the limited data set often accompanying patients with AWS, flumazenil diagnosed benzodiazepine delirium during the treatment of AWS and improved impairments in cognition and behavior without serious or life-threatening adverse events in our patients.

A preliminary study of tricyclic antidepressant (TCA) ovine FAB for TCA toxicity.

BACKGROUND: Animal studies suggest that tricyclic antidepressant antibody fragments (TCA Fab) may be a useful therapy for tricyclic antidepressant poisoning. The objective of this study is to determine if TCA Fab increases total serum TCA levels without raising free serum TCA levels in human overdose patients, indicating that TCA Fab effectively binds TCA. METHODS: This was a prospective, dose escalation study of patients with mild to moderate TCA poisoning. Patients were treated with an escalating intravenous infusion totaling 7 or 14 gm of TCA Fab. The outcomes of interest were serum TCA levels (total and free), worsening of TCA toxicity, and adverse effects. RESULTS: Seven patients were treated with Fab. Infusion of TCA Fab was associated with a dramatic increase in total serum TCA levels, while free TCA levels fell in both dosing groups. There were no significant changes in QRS duration, heart rate or mean arterial pressure associated with the Fab Infusion. Worsening of TCA toxicity did not occur despite marked elevation of total serum TCA concentrations. The two patients with the greatest prolongation of QRS showed a prompt shortening in their QRS duration temporally associated with the Fab infusion. Mild wheezing was observed in one asthmatic patient. CONCLUSIONS: 1) TCA Fab raises total serum TCA levels while lowering free levels in TCA poisoned patients; 2) no toxic effects were associated with the increase in TCA levels and no severe adverse effects were observed during the hospital course following Fab infusion.

Symptom-based, algorithmic approach for handling the initial encounter with victims of a potential terrorist attack.

OBJECTIVES: This study intended to create symptom-based triage algorithms for the initial encounter with terror-attack victims. The goals of the triage algorithms include: (1) early recognition; (2) avoiding contamination; (3) early use of antidotes; (4) appropriate handling of unstable, contaminated victims; and (5) provisions of force protection. The algorithms also address industrial accidents and emerging infections, which have similar clinical presentations and risks for contamination as weapons of mass destruction (WMD). METHODS: The algorithms were developed using references from military and civilian sources. They were tested and adjusted using a series of theoretical patients from a CD-ROM chemical, biological, radiological/nuclear, and explosive victim simulator. Then, the algorithms were placed into a card format and sent to experts in relevant fields for academic review. RESULTS: Six inter-connected algorithms were created, described, and presented in figure form. The "attack" algorithm, for example, begins by differentiating between overt and covert attack victims (A covert attack is defined by epidemiological criteria adapted from the Centers for Disease Control and Prevention (CDC) recommendations). The attack algorithm then categorizes patients either as stable or unstable. Unstable patients flow to the "Dirty Resuscitation" algorithm, whereas, stable patients flow to the "Chemical Agent" and "Biological Agent" algorithms. The two remaining algorithms include the "Suicide Bomb/Blast/Explosion" and the "Radiation Dispersal Device" algorithms, which are inter-connected through the overt pathway in the "Attack" algorithm. CONCLUSION: A civilian, symptom-based, algorithmic approach to the initial encounter with victims of terrorist attacks, industrial accidents, or emerging infections was created. Future studies will address the usability of the algorithms with theoretical cases and utility in prospective, announced and unannounced, field drills. Additionally, future studies will assess the effectiveness of teaching modalities used to reinforce the algorithmic approach.