A Statistical Model to Predict Protection Against Infant Respiratory Syncytial Virus Disease Through Maternal Immunization
Background/Objectives: Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants worldwide. Maternal immunization to protect younger infants is supported by evidence that virus-neutralizing antibodies, which are efficiently transferred across the placenta from m...
| Published in: | Vaccines |
|---|---|
| Main Authors: | , , , , , , , , |
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-393X/12/12/1351 |
| _version_ | 1850029870747222016 |
|---|---|
| author | Bing Cai Yili Chen Yasmeen Agosti Beate Schmoele-Thoma Kenneth Koury Kathrin U. Jansen William C. Gruber Philip R. Dormitzer Kena A. Swanson |
| author_facet | Bing Cai Yili Chen Yasmeen Agosti Beate Schmoele-Thoma Kenneth Koury Kathrin U. Jansen William C. Gruber Philip R. Dormitzer Kena A. Swanson |
| author_sort | Bing Cai |
| collection | DOAJ |
| container_title | Vaccines |
| description | Background/Objectives: Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants worldwide. Maternal immunization to protect younger infants is supported by evidence that virus-neutralizing antibodies, which are efficiently transferred across the placenta from mother to fetus, are a primary immune mediator of protection. In maternal RSV vaccine studies, estimates of correlates of protection are elusive because many factors of maternal–fetal immunobiology and disease characteristics must be considered for the estimates. Methods: We developed statistical models that aims to predict vaccine efficacy (VE) in infants following maternal immunization by including quantifiable covariates of the antibody titer distribution of the mother (pre- and post-immunization), the transplacental transfer ratio of IgG antibodies, the rate of antibody decay, and RSV disease incidence rate, all of which are season- and time-dependent and vary by infant age. Result: Our model shows that integrating the lower respiratory tract disease risk based on infant airway diameter and associated airway resistance is critical to appropriately model predicted infant VE. The VE predictions by our models, which preceded maternal RSV prefusion F vaccine efficacy trial primary readouts, closely align with the VE outcomes of these field studies. Conclusion: Our models successfully predicted VE of the RSV maternal vaccines and have potential use in modeling the clinical trial out-comes of other respiratory disease vaccines where maternal antibodies play a role in the protection of newborns. |
| format | Article |
| id | doaj-art-888cc52b525f4068bf239df014f9fa21 |
| institution | Directory of Open Access Journals |
| issn | 2076-393X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| spelling | doaj-art-888cc52b525f4068bf239df014f9fa212025-08-20T00:36:29ZengMDPI AGVaccines2076-393X2024-11-011212135110.3390/vaccines12121351A Statistical Model to Predict Protection Against Infant Respiratory Syncytial Virus Disease Through Maternal ImmunizationBing Cai0Yili Chen1Yasmeen Agosti2Beate Schmoele-Thoma3Kenneth Koury4Kathrin U. Jansen5William C. Gruber6Philip R. Dormitzer7Kena A. Swanson8Vaccine Research and Development, Pfizer Inc., Collegeville, PA 19426, USAClinical Development and Operations, Pfizer Inc., Collegeville, PA 19426, USAChief Medical Affairs Office, Pfizer Inc., Collegeville, PA 19426, USAVaccine Research and Development, Pfizer Inc., Pearl River, NY 10965, USAVaccine Research and Development, Pfizer Inc., Pearl River, NY 10965, USAVaccine Research and Development, Pfizer Inc., Pearl River, NY 10965, USAVaccine Research and Development, Pfizer Inc., Pearl River, NY 10965, USAVaccine Research and Development, Pfizer Inc., Pearl River, NY 10965, USAVaccine Research and Development, Pfizer Inc., Pearl River, NY 10965, USABackground/Objectives: Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants worldwide. Maternal immunization to protect younger infants is supported by evidence that virus-neutralizing antibodies, which are efficiently transferred across the placenta from mother to fetus, are a primary immune mediator of protection. In maternal RSV vaccine studies, estimates of correlates of protection are elusive because many factors of maternal–fetal immunobiology and disease characteristics must be considered for the estimates. Methods: We developed statistical models that aims to predict vaccine efficacy (VE) in infants following maternal immunization by including quantifiable covariates of the antibody titer distribution of the mother (pre- and post-immunization), the transplacental transfer ratio of IgG antibodies, the rate of antibody decay, and RSV disease incidence rate, all of which are season- and time-dependent and vary by infant age. Result: Our model shows that integrating the lower respiratory tract disease risk based on infant airway diameter and associated airway resistance is critical to appropriately model predicted infant VE. The VE predictions by our models, which preceded maternal RSV prefusion F vaccine efficacy trial primary readouts, closely align with the VE outcomes of these field studies. Conclusion: Our models successfully predicted VE of the RSV maternal vaccines and have potential use in modeling the clinical trial out-comes of other respiratory disease vaccines where maternal antibodies play a role in the protection of newborns.https://www.mdpi.com/2076-393X/12/12/1351RSVviral infectioninfantsairway resistancemodelingvaccine efficacy |
| spellingShingle | Bing Cai Yili Chen Yasmeen Agosti Beate Schmoele-Thoma Kenneth Koury Kathrin U. Jansen William C. Gruber Philip R. Dormitzer Kena A. Swanson A Statistical Model to Predict Protection Against Infant Respiratory Syncytial Virus Disease Through Maternal Immunization RSV viral infection infants airway resistance modeling vaccine efficacy |
| title | A Statistical Model to Predict Protection Against Infant Respiratory Syncytial Virus Disease Through Maternal Immunization |
| title_full | A Statistical Model to Predict Protection Against Infant Respiratory Syncytial Virus Disease Through Maternal Immunization |
| title_fullStr | A Statistical Model to Predict Protection Against Infant Respiratory Syncytial Virus Disease Through Maternal Immunization |
| title_full_unstemmed | A Statistical Model to Predict Protection Against Infant Respiratory Syncytial Virus Disease Through Maternal Immunization |
| title_short | A Statistical Model to Predict Protection Against Infant Respiratory Syncytial Virus Disease Through Maternal Immunization |
| title_sort | statistical model to predict protection against infant respiratory syncytial virus disease through maternal immunization |
| topic | RSV viral infection infants airway resistance modeling vaccine efficacy |
| url | https://www.mdpi.com/2076-393X/12/12/1351 |
| work_keys_str_mv | AT bingcai astatisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT yilichen astatisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT yasmeenagosti astatisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT beateschmoelethoma astatisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT kennethkoury astatisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT kathrinujansen astatisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT williamcgruber astatisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT philiprdormitzer astatisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT kenaaswanson astatisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT bingcai statisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT yilichen statisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT yasmeenagosti statisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT beateschmoelethoma statisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT kennethkoury statisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT kathrinujansen statisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT williamcgruber statisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT philiprdormitzer statisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization AT kenaaswanson statisticalmodeltopredictprotectionagainstinfantrespiratorysyncytialvirusdiseasethroughmaternalimmunization |
