Top 7 Predictive Model Applications in Drug Safety and Pharmacovigilance

As drug safety and pharmacovigilance organizations develop more sophisticated data analytics capabilities, they are starting to move from basic descriptive analysis towards predictive analysis and the development of predictive models. Predictive analytics uses existing information to make predictions of future outcomes or future trends in all areas of Medicine and Health Care¹.

The importance of being one step ahead of (adverse) events is most clearly seen in the framework of signal detection, and of the identification and characterization of individuals with a specific risk for developing an adverse event after the exposure to a medicine, both in clinical development^2,3 and in post-marketing settings⁴.

Identification of risks from spontaneous reports

Predictive modeling can be used for the identification of previously unrecognized risks of medicines in pharmacovigilance reports. A nice example of this use is VigiRank, a data-driven predictive model for emerging safety signals, which has been shown to outperform disproportionality analysis alone in real world pharmacovigilance signal detection⁵. VigiRank is to be applied in VigiBase, in which predictive models have been proven useful to detect safety signals that were eventually validated, in pediatric populations.⁶

Evaluation of unexpected increase in reporting frequency

Similarly, the European Medicines Agency developed an algorithm to detect unexpected increases in frequencies of reports, in particular quality defects, medication errors, and cases of abuse or misuse. The algorithm applied to the EudraVigilance database showed encouraging results⁷.

Risk prediction of adverse experiences after exposure to a drug

Predictive models have been also used to predict the relationship between exposure to an investigational medicinal product and the risk of adverse events. For example, Niebecker⁸ characterized the relationship between exposure to afatinib and diarrhea and rash/acne adverse event trajectories, with the final goal of developing a modeling framework to allow prospective comparison of dosing strategies and study designs with respect to safety. In another other example, predictive models have been used for the prediction of adverse reactions after administration of rituximab in patients with hematologic malignancies⁹.

Different approaches to predictive analysis have been taken, depending on the specific machine learning tool applied. Machine learning has been used to predict the probability of adverse event occurrence at the time of drug prescribing, using a neural network model.¹⁰

Predictive models in clinical development and postmarket signal detection

Other authors developed a model to quantify whether safety signals observed in first-in-human studies were likely the result of chance or the compound under investigation. The model quantifies how likely an event is due to chance, conditionally on the characteristics of the subject and the study¹¹.

The combination of different predictive modeling techniques like random forest, L1 regularized logistic regression, support vector machine, and neural models were successfully applied to detect signals arising from laboratory-event-related adverse drug reactions. The authors combined features from each of the modeling techniques into a machine learning model. The application of this model to an electronic health record environment was considered satisfactory for signal detection purposes¹².

Supervised machine learning signal detection methods have been tested for the identification of adverse drug reactions. In the world of medication dispensing data, sequence symmetry analysis (SSA) has been used to detect signals of adverse drug reactions. This precise study shows how a gradient boost classifier complements well SSA¹³.

Specific subpopulations like hospitalized patients

Predictive analysis and model development shows interesting uses in the evaluation of risks as in this case, where the authors used mathematical models to determine the probability of adverse drug experiences in the surgical setting at the time of hospital admission, identifying the patients that are at a higher risk of an adverse drug experience during the hospital stay¹⁴. In another study focused on drug safety in hospitals, the authors perform a systematic review of predictive risk models for adverse drug events during hospitalization¹⁵.

Prediction of hepatotoxicity and interactions

To predict drug-induced hepatotoxicity based on gene expression and toxicology data, by means of a multi-dose computational model¹⁶.

Use of predictive models for the prediction of adverse drug reactions induced by drug-drug interactions¹⁷.

Predictive models for comparative safety

Leonard CE et al. utilized a Cox proportional hazard model to identify comparative safety differences among 3 sulfonylureas and the risk of sudden cardiac arrest and ventricular arrhythmia¹⁸.

Jose Rossello