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 development2,3 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.
- 1.Alanazi H, Abdullah A, Qureshi K. A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care. J Med Syst. 2017;41(4):69. https://www.ncbi.nlm.nih.gov/pubmed/28285459.
- 2.Federer C, Yoo M, Tan A. Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials. Assay Drug Dev Technol. 2016;14(10):557-566. https://www.ncbi.nlm.nih.gov/pubmed/27631620.
- 3.Poleksic A, Xie L. Predicting serious rare adverse reactions of novel chemicals. Bioinformatics. 2018;34(16):2835-2842. https://www.ncbi.nlm.nih.gov/pubmed/29617731.
- 4.Ventola C. Big Data and Pharmacovigilance: Data Mining for Adverse Drug Events and Interactions. P T. 2018;43(6):340-351. https://www.ncbi.nlm.nih.gov/pubmed/29896033.
- 5.Caster O, Sandberg L, Bergvall T, Watson S, Norén G. vigiRank for statistical signal detection in pharmacovigilance: First results from prospective real-world use. Pharmacoepidemiol Drug Saf. 2017;26(8):1006-1010. https://www.ncbi.nlm.nih.gov/pubmed/28653790.
- 6.Star K, Sandberg L, Bergvall T, Choonara I, Caduff-Janosa P, Edwards I. Paediatric safety signals identified in VigiBase: Methods and results from Uppsala Monitoring Centre. Pharmacoepidemiol Drug Saf. February 2019. https://www.ncbi.nlm.nih.gov/pubmed/30767342.
- 7.Pinheiro L, Candore G, Zaccaria C, Slattery J, Arlett P. An algorithm to detect unexpected increases in frequency of reports of adverse events in EudraVigilance. Pharmacoepidemiol Drug Saf. 2018;27(1):38-45. https://www.ncbi.nlm.nih.gov/pubmed/29143393.
- 8.Niebecker R, Maas H, Staab A, Freiwald M, Karlsson M. Modelling Exposure-Driven Adverse Event Time Courses in Oncology Exemplified by Afatinib. CPT Pharmacometrics Syst Pharmacol. January 2019. https://www.ncbi.nlm.nih.gov/pubmed/30681293.
- 9.D’Arena G, Simeon V, Laurenti L, et al. Adverse drug reactions after intravenous rituximab infusion are more common in hematologic malignancies than in autoimmune disorders and can be predicted by the combination of few clinical and laboratory parameters: results from a retrospective, multicenter study of 374 patients. Leuk Lymphoma. 2017;58(11):2633-2641. https://www.ncbi.nlm.nih.gov/pubmed/28367662.
- 10.Kasatkin D, Bogomolov Y, Spirin N. [Steps to personalized therapy of multiple sclerosis: predicting safety of treatment using mathematical modeling]. Zh Nevrol Psikhiatr Im S S Korsakova. 2018;118(8. Vyp. 2):70-76. https://www.ncbi.nlm.nih.gov/pubmed/30160671.
- 11.Clayton G, Schachter A, Magnusson B, Li Y, Colin L. How Often Do Safety Signals Occur by Chance in First-in-Human Trials? Clin Transl Sci. 2018;11(5):471-476. https://www.ncbi.nlm.nih.gov/pubmed/29702733.
- 12.Jeong E, Park N, Choi Y, Park R, Yoon D. Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals. PLoS One. 2018;13(11):e0207749. https://www.ncbi.nlm.nih.gov/pubmed/30462745.
- 13.Hoang T, Liu J, Roughead E, Pratt N, Li J. Supervised signal detection for adverse drug reactions in medication dispensing data. Comput Methods Programs Biomed. 2018;161:25-38. https://www.ncbi.nlm.nih.gov/pubmed/29852965.
- 14.Bos J, Kalkman G, Groenewoud H, et al. Prediction of clinically relevant adverse drug events in surgical patients. PLoS One. 2018;13(8):e0201645. https://www.ncbi.nlm.nih.gov/pubmed/30138343.
- 15.Falconer N, Barras M, Cottrell N. Systematic review of predictive risk models for adverse drug events in hospitalized patients. Br J Clin Pharmacol. 2018;84(5):846-864. https://www.ncbi.nlm.nih.gov/pubmed/29337387.
- 16.Su R, Wu H, Xu B, Liu X, Wei L. Developing a Multi-Dose Computational Model for Drug-induced Hepatotoxicity Prediction based on Toxicogenomics Data. IEEE/ACM Trans Comput Biol Bioinform. July 2018. https://www.ncbi.nlm.nih.gov/pubmed/30040651.
- 17.Liu R, AbdulHameed M, Kumar K, Yu X, Wallqvist A, Reifman J. Data-driven prediction of adverse drug reactions induced by drug-drug interactions. BMC Pharmacol Toxicol. 2017;18(1):44. https://www.ncbi.nlm.nih.gov/pubmed/28595649.
- 18.Leonard C, Brensinger C, Aquilante C, et al. Comparative Safety of Sulfonylureas and the Risk of Sudden Cardiac Arrest and Ventricular Arrhythmia. Diabetes Care. 2018;41(4):713-722. https://www.ncbi.nlm.nih.gov/pubmed/29437823.
Virgilio Vinas says
Molt be. Excelente articulo..
The future of PV is here. How to make feasible and accessible is challenging.
Sunil Nighot says
dear Jose, thank you so much for sharing the various data anlaytics tools which can be help to improvise the PV process. I need to connect with you.
Thanks and Kind Regards