The revised guidance on Screening for adverse reactions in EudraVigilance describes methods for screening adverse drug reactions used by the European Medicines Agency and national competent authorities. The proposed methods complement the classical disproportionality analysis with additional data summaries, based on both statistical and clinical considerations. This approach is based on the fact that, although disproportionality methods have demonstrated to detect many adverse reactions before other currently used methods of signal detection, this is not true for all types of adverse reactions.
Hence a comprehensive and efficient routine signal detection system will seek to integrate a number of different methods to prioritise the drug event combinations for further evaluation. For the methods recommended, the guidance addresses elements of their interpretation, their potential advantages and limitations and the evidence behind. Areas of uncertainty that require resolution before firm recommendations can be made are also mentioned.
As understanding increases regarding the mechanisms at a molecular level that are involved in adverse effects of drugs it would be expected that this information will inform efforts to predict and detect drug safety problems. Such modelling is presented in Data-driven prediction of drug effects and interactions (Sci Transl Med. 2012 14;4(125):125ra31) and should be a major focus of drug safety research activities. An example of an application of this concept is illustrated in the paper Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome (J Am Med Inform Assoc. 2016; 23(5): 968–78) where the authors apply a Quantitative Structure-Activity Relationship (QSAR) model to predict the drugs associated with Stevens Johnson syndrome in a pharmacovigilance database. In Target Adverse Event Profiles for Predictive Safety in the Postmarket Setting (Clin Pharmacol Ther. 2021;109(5):1232-43), the authors identify drugs that share pharmacological targets with the drug of interest and use information from these drugs to predict post-marketing adverse drug reactions of the drug of interest. Machine learning on data from the FDA Adverse Event Reporting System, peer-reviewed literature and FDA drug labels is used for the prediction. In Role of serotonin and norepinephrine transporters in antidepressant-induced arterial hypertension: a pharmacoepidemiological-pharmacodynamic study (Eur J Clin Pharmacol. 2020 Sep;76(9):1321-1327.) disproportionality analysis on Vigibase was combined with a pharmacodynamic study to study the relationship between SRIs ands SNRIs and arterial hypertension, taking in to account the affinity for noradrenergic and serotonergic receptors.
With pharmacovigilance databases increasing in size, manual review of all cases becomes a non-scalable process both because the increasing number of cases to review in each potential signal and because it is difficult to summarise hundreds of case reports in a narrative format. To address some of these issues there has been recent experimentation with machine learning and natural language processing techniques. Towards Automating Adverse Event Review: A Prediction Model for Case Report Utility (Drug Saf. 2020 Apr;43(4):329-338) notes the need to develop modernised pharmacovigilance practices and shows the feasibility of developing a tool predictive of ICSR utility. Feature engineering and machine learning for causality assessment in pharmacovigilance: Lessons learned from application to the FDA Adverse Event Reporting System (Comput Biol Med. 2021 Aug;135:104517) describes the use of machine learning techniques to quickly eliminate non-assessable reports.