title: Analyzing Complex Antibody Profiles to Inform Malaria Vaccine Development
Creators: name: Wistuba-Hamprecht, Jacqueline
Publisher: name: Universität Tübingen
Year: 2026
Subjects: subject: Malaria , Maschinelles Lernen , Erklärbare künstliche Intelligenz , Impfstoff

description: Despite being treatable and preventable, malaria remains a major global health problem, responsible for over 600,000 deaths in 2022 alone. To date, the RTS,S/AS01 (RTS,S) and R21/Matrix-M (R21) vaccines are the first two licensed malaria vaccines recommended by theWorld Health Organization for the prevention of Plasmodium falciparum(P. falciparum)malaria in children living in endemic areas. Both, RTS,S and R21 are subunit vaccines that provide partial protection for about a year. Despite the success of these first-generation vaccines, there remains an urgent need to developmore effective malaria vaccines. These new vaccines should ideally be suitable for broader use, including for pre-exposed adults in endemic regions, pregnant women, and malaria-naïve travelers. Moreover, they should confer long-lasting sterile protection and help reduce malaria transmission. Notably, sterile protection against P. falciparum has been achieved with new vaccine platforms, such as radiation-attenuated P. falciparum sporozoites (PfSPZ Vaccine) and chemo-attenuated PfSPZ (PfSPZ-CVac) inmalaria-naïve volunteers. However, these vaccines generally show lower efficacy in pre-exposed residents of endemic regions. The protection induced by these SPZ-based vaccines primarily occurs during the pre-erythrocytic stages and is largely mediated by cellular and antibody responses. Therefore, a deeper understanding of the vaccine-induced immune response in both, malaria-naïve individuals and those living in endemic regions, as well as the identification of new potential antigen, is crucial for the development of improved vaccine candidates. Machine learning has become increasingly popular in medical research over the past few decades, significantly improving our understanding of complex medical data. However, machine learning faces significant challenges when applied tomedical data obtained in early stage clinical development, particularly in the context of vaccine efficacy assessment against P. falciparum. Such data is often high dimensional, limited in sample size, and heterogeneous. Consequently,machine learning in malaria research requires methods that can effectively handle small, high dimensional, and heterogeneous datasets. Moreover, identifying and interpreting P. falciparum-specific antigens involved in vaccine-mediated and naturally acquired protection necessitates interpretation techniques capable of managing highdimensional datasets while accounting for correlations between features. This thesis addresses both aspects by developing machine learning methods tailored to small, high-dimensional datasets and inventing interpretation techniques to evaluate informative P. falciparum-specific antigens associated with protection frommalaria. To this end, a multitask support vector machine approach was adapted to combine time- and PfSPZ-CVac dose-dependent data, enhancing vaccine efficacy prediction compared to state-of-the-art methods in a study population of malaria-naïve volunteers. Additionally, a kernel-based interpretation technique was developed to evaluate P. falciparum-specific antigens in the classification of protected versus non-protected volunteers. Another task-specific contribution was the development of a benchmark for training machine learning models to explore P. falciparum protein vaccine and drug candidates. Furthermore, this thesis presents a workflow for appropriately applying machine learning methods to predict vaccine efficacy across different vaccine types, comparing pre-exposed and malaria-naïve adult volunteers. While the results of this thesis show promising advancements in the application of machine learning to the analysis of malaria vaccine-induced humoral immunity, they also highlight the challenges of applying machine learning methods to small, high-dimensional datasets derived from early stage clinical trials.
description: Die Dissertation ist gesperrt bis zum 8. Dezember 2026 !