BACKGROUND: The potential for development of autoimmune diseases after vaccination with new vaccines containing novel adjuvants is a theoretical concern. Randomised, placebo-controlled trials are the best method for assessing a potential causal relationship between an adverse event and vaccination, but usually have a sample size too small to detect adverse events occurring in <1% of subjects. Incomplete case documentation may hamper definitive diagnoses, preventing accurate causality assessment. To date there are no guidelines for collection, documentation and monitoring of potential immune mediated disorders (pIMD) reported in the course of clinical trials with adjuvanted vaccines. OBJECTIVE: This paper proposes a methodology for collection of pIMDs in clinical vaccine trials, with the objective of obtaining complete and reliable data using standardised methodology for its collection and analysis. RECOMMENDATIONS: The role of the study investigator in prospective, standardised safety data collection is key and can be facilitated by providing a pIMD list in study documents and disease-specific standard questionnaires to assist timely and thorough documentation. External expert review of histopathology samples or other specialised diagnostic data would increase diagnostic accuracy. Centralised case ascertainment using standard case definitions would identify true cases of interest. We propose collection of safety data for at least 6 months and up to one year after the last vaccine dose. Bio-banking as a platform for collecting samples from enrolled patients for future use (e.g., to measure biomarkers of diagnostic, prognostic or predictive utility) could eventually provide valuable information in cases where a pIMD is diagnosed during the study period. CONCLUSION: Standardised collection of safety data to allow appropriate analyses are optimal approaches for detecting rare events in clinical trials. Appropriate data analysis will then more reliably define potential causal relationships with vaccination.
BACKGROUND: The potential for development of autoimmune diseases after vaccination with new vaccines containing novel adjuvants is a theoretical concern. Randomised, placebo-controlled trials are the best method for assessing a potential causal relationship between an adverse event and vaccination, but usually have a sample size too small to detect adverse events occurring in <1% of subjects. Incomplete case documentation may hamper definitive diagnoses, preventing accurate causality assessment. To date there are no guidelines for collection, documentation and monitoring of potential immune mediated disorders (pIMD) reported in the course of clinical trials with adjuvanted vaccines. OBJECTIVE: This paper proposes a methodology for collection of pIMDs in clinical vaccine trials, with the objective of obtaining complete and reliable data using standardised methodology for its collection and analysis. RECOMMENDATIONS: The role of the study investigator in prospective, standardised safety data collection is key and can be facilitated by providing a pIMD list in study documents and disease-specific standard questionnaires to assist timely and thorough documentation. External expert review of histopathology samples or other specialised diagnostic data would increase diagnostic accuracy. Centralised case ascertainment using standard case definitions would identify true cases of interest. We propose collection of safety data for at least 6 months and up to one year after the last vaccine dose. Bio-banking as a platform for collecting samples from enrolled patients for future use (e.g., to measure biomarkers of diagnostic, prognostic or predictive utility) could eventually provide valuable information in cases where a pIMD is diagnosed during the study period. CONCLUSION: Standardised collection of safety data to allow appropriate analyses are optimal approaches for detecting rare events in clinical trials. Appropriate data analysis will then more reliably define potential causal relationships with vaccination.
Authors: Adriana Bastidas; Javier de la Serna; Mohamed El Idrissi; Lidia Oostvogels; Philippe Quittet; Javier López-Jiménez; Filiz Vural; David Pohlreich; Tsila Zuckerman; Nicolas C Issa; Gianluca Gaidano; Je-Jung Lee; Sunil Abhyankar; Carlos Solano; Jaime Perez de Oteyza; Michael J Satlin; Stefan Schwartz; Magda Campins; Alberto Rocci; Carlos Vallejo Llamas; Dong-Gun Lee; Sen Mui Tan; Anna M Johnston; Andrew Grigg; Michael J Boeckh; Laura Campora; Marta Lopez-Fauqued; Thomas C Heineman; Edward A Stadtmauer; Keith M Sullivan Journal: JAMA Date: 2019-07-09 Impact factor: 56.272
Authors: Joon Hyung Kim; John Diaz-Decaro; Ning Jiang; Shinn-Jang Hwang; Eun Ju Choo; Maribel Co; Andrew Hastie; David Shu Cheong Hui; Junya Irimajiri; Jacob Lee; Edward Man-Fuk Leung; Haiwen Tang; Tomomi Tsuru; Philip Watson; Zhenhua Wu; Chong-Jen Yu; Yanfei Yuan; Toufik Zahaf; Anthony L Cunningham; Anne Schuind Journal: Hum Vaccin Immunother Date: 2021-02-19 Impact factor: 3.452
Authors: Fernanda Tavares Da Silva; Alberta Di Pasquale; Juan P Yarzabal; Nathalie Garçon Journal: Hum Vaccin Immunother Date: 2015 Impact factor: 3.452
Authors: Marta López-Fauqued; Maribel Co-van der Mee; Adriana Bastidas; Pierre Beukelaers; Alemnew F Dagnew; Juan Jose Fernandez Garcia; Anne Schuind; Fernanda Tavares-da-Silva Journal: Drug Saf Date: 2021-06-11 Impact factor: 5.606
Authors: Maria-Genalin Angelo; Julia Zima; Fernanda Tavares Da Silva; Laurence Baril; Felix Arellano Journal: Pharmacoepidemiol Drug Saf Date: 2014-02-20 Impact factor: 2.890
Authors: Maria-Genalin Angelo; Marie-Pierre David; Julia Zima; Laurence Baril; Gary Dubin; Felix Arellano; Frank Struyf Journal: Pharmacoepidemiol Drug Saf Date: 2014-02-20 Impact factor: 2.890
Authors: David W Vaughn; Harry Seifert; Anne Hepburn; Walthere Dewe; Ping Li; Mamadou Drame; Catherine Cohet; Bruce L Innis; Louis F Fries Journal: Hum Vaccin Immunother Date: 2014-11-21 Impact factor: 3.452