Automated Detection of Off-Label Drug Use

<div><p>Off-label drug use, defined as use of a drug in a manner that deviates from its approved use defined by the drug's FDA label, is problematic because such uses have not been evaluated for safety and efficacy. Studies estimate that 21% of prescriptions are off-label, and only 27% of those have evidence of safety and efficacy. We describe a data-mining approach for systematically identifying off-label usages using features derived from free text clinical notes and features extracted from two databases on known usage (Medi-Span and DrugBank). We trained a highly accurate predictive model that detects novel off-label uses among 1,602 unique drugs and 1,472 unique indications. We validated 403 predicted uses across independent data sources. Finally, we prioritize well-supported novel usages for further investigation on the basis of drug safety and cost.</p></div

Overview of methods and results.

<p>For each of the 2,362,950 possible drug-indication pairs, we calculated 9 empirical features (e.g., co-mention count) from the free text of clinical notes in STRIDE and 16 domain knowledge features (e.g., similarity in known usage to other drugs used to treat the indication) from Medi-Span and Drugbank. These features were used by an SVM classifier trained on a gold standard dataset to recognize the used-to-treat relationship, yielding a set of predictions that were filtered for known usages, near misses in the indications, and support in two independent and complementary datasets (FAERS and MEDLINE). Predicted usages that appeared to be drug adverse events listed in SIDER 2 were removed. The resulting set of 403 well-supported novel off-label usages were binned using indices of risk and cost.</p

Predicted off-label usages binned by risk and cost and ranked by support in FAERS.

<p>We ranked predicted, novel off-label usages on the basis of risk and cost, as represented by our risk and cost indices for each drug. FAERS Support for each drug-indication pair is the number of distinct case reports in FAERS in which the drug was explicitly listed as being used to treat the indication. The risk index is a quantitative score that represents the expected disutility of adverse events related to the use of the drug in question, normalized to the range [0, 1] so that drugs that have a higher risk of causing serious adverse events have higher values. The cost index is based on the mean unit cost of the drug in question in Medi-Span, normalized to the range [0, 1] with more expensive drugs having a higher value.</p

Performance of classifier on hold-out test set using different feature sets.

<p>We performed feature ablation experiments to assess the contribution of different feature sets to the performance of the classifier for detecting used-to-treat relationships. The first column indicates the features used to train and test the classifiers. Classifier performance was evaluated in a hold out test set of 1,749 positive and 7,035 negative examples of drug usage after training in a set of 7,112 positive and 27,938 negative examples. The first row shows performance using STRIDE derived features in which co-mentions are counted without regard to present known indications in the clinical record.</p

Training and testing a classifier to recognize used-to-treat relationships.

<p>We created a gold standard of positive and negative examples of known drug usage. Positive examples were taken from Medi-Span. We created negative examples by randomly selecting positive examples and then randomly choosing a drug and indication with roughly the same frequency of mentions in STRIDE as the real usage. These were then checked against Medi-Span to filter out inadvertently generated known usages. The gold standard dataset contained 4 negative examples for each positive case. For each drug-indication pair in the gold standard, we calculated features summarizing the pattern of mentions of the drugs and indications in 9.5 million clinical notes from STRIDE. We used Medi-Span and Drugbank to calculate features summarizing domain knowledge about drugs and their usages. 80% of the gold standard was used to train an SVM classifier, and the resulting model was tested on the remaining 20%.</p

Selected predicted novel off-label usages.

<p>Predicted, novel drug usages with substantial support in FAERS. FAERS Support for each drug-indication pair is the number of distinct case reports in FAERS in which the drug was explicitly listed as being used to treat the indication. A complete listing is available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089324#pone.0089324.s001" target="_blank">Table S1</a>.</p

Distribution of indication classes in predicted novel usages.

<p>Each indication for the 403 high confidence novel usages with support in FAERS and MEDLINE was mapped to the first level of the NDF-RT disease hierarchy. 63 usages were not mapped to NDF-RT and were left out of this chart.</p

Supplemental Data: A Network-Biology Informed Computational Drug Repositioning Strategy to Target Disease Risk Trajectories and Comorbidities of Peripheral Arterial Disease.

<div><div>Supplemental Data: </div><div>A Network-Biology Informed Computational Drug Repositioning Strategy to Target Disease Risk Trajectories and Comorbidities of Peripheral Arterial Disease. </div></div><div><br></div

Candidate causal regulators of the Blue-mmSS module are upstream of HD-relevant nodes.

<p>(A) Bayesian network reconstruction of the Blue-mmSS reveals 26 CCRs (large labeled nodes). (B) Genes in Thistle2-hsHD, especially those with FOXO3 binding sites, are significantly overrepresented in the Blue-mmSS downstream network. Significance threshold (red line): P = 0.01, two-sided. (C) Enrichment of the causal regulator PIN for huntingtin protein binding partners identified by affinity purification-mass spectrometry (AP-MS) and yeast two-hybrid (Y2H) methods, and for genes necessary for voluntary movement and affective behavior. Significance thresholds (red line): P = 0.01, two-sided; Odds Ratio (95% Confidence Interval) = 2. (D) Drugs that concordantly upregulate (Drug-Causal Regulator Association Score > 0) and downregulate (Drug-Causal Reulgator Association Score < 0) Blue-mmSS CCRs. All drugs shown have Benjamini-Hochberg adjusted P < 0.05.</p

Coexpression networks in the CN, CB, and CTX of a human HD cohort.

<p>Topological overlap (TO) matrix plot depicts gene coexpression networks in (A) CN, (C) CB, and (E) CTX. (B,D,F) A comparison of TO matrices in cases (top right triangle) versus controls (bottom left triangle) for selected modules in each brain region. High TO (greater coexpression) is colored red, while low TO is colored white. Module differential connectivity (MDC) and FDR values are depicted for each module. Differential connectivity was considered significant by conservative thresholds (MDC > 2.0 or MDC < 0.5, FDR < 0.001). Three differentially connected modules and one conserved module are depicted for each brain region.</p