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. 2018 Feb;103(2):177-179.
doi: 10.1002/cpt.949.

The Next Generation of Drug Safety Science: Coupling Detection, Corroboration, and Validation to Discover Novel Drug Effects and Drug-Drug Interactions

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The Next Generation of Drug Safety Science: Coupling Detection, Corroboration, and Validation to Discover Novel Drug Effects and Drug-Drug Interactions

Nicholas P Tatonetti. Clin Pharmacol Ther. .
Free PMC article

Abstract

Rare adverse drug reactions and drug-drug interactions (DDIs) are difficult to detect in randomized trials and impossible to prove using observational studies. We must ascribe to a new way of conducting research that has the efficiency of a retrospective analysis and the rigor of a prospective trial. This can be achieved by integrating observational data from humans with laboratory experiments in model systems. The former establishes clinical significance and the latter supports causality.

Conflict of interest statement

Conflict of Interest

Author is a paid advisor to Advera Health, Inc. Author declares no other conflicts of interest.

Figures

Figure 1
Figure 1. The three steps to more rigorous and efficient drugs safety surveillance
Step 1. Detection. Modern signal detection and statistical data mining method are used to identify new drug safety and drug-drug interaction signals. Methods include traditional approaches, like disproportionality analysis with statistical corrections, and newer methods, like supervised machine learning and pattern detection. This step produces a lot of statistically significant associations but also a lot of false discoveries. Step 2. Corroboration. Mined hypotheses are evaluated against an independent dataset and evaluated for plausibility. These additional data could score drug effect hypotheses by their molecular connection to the known effect or provide additional clinical evidence from alternative resources. Any hypotheses that does not corroborate is removed from consideration, greatly reduces the number of false discoveries. At the end of this stage only a few to dozens of the strongest hypotheses remain. Step 3. Validation. Corroborated hypotheses are validated using a model system. Model systems may be molecular assays (e.g. chemical-protein binding affinity), cellular systems, or animal models, depending on which model is best suited for the predicted adverse reaction outcome. Drug effects that validate in all three steps have demonstrated clinical importance in humans (steps 1 and 2) and have evidence of causality (step 3). This process is both more efficient than a clinical trial and more rigorous than a retrospective analysis.

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