Moving the needle: Employing deep reinforcement learning to push the boundaries of coarse-grained vaccine models.

Autor:	Faris JG; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, United States., Orbidan D; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, United States., Wells C; Department of Computer Science, Rice University, TX, Houston, United States., Petersen BK; Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, United States., Sprenger KG; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in immunology [Front Immunol] 2022 Nov 03; Vol. 13, pp. 1029167. Date of Electronic Publication: 2022 Nov 03 (Print Publication: 2022).
DOI:	10.3389/fimmu.2022.1029167
Abstrakt:	Highly mutable infectious disease pathogens (hm-IDPs) such as HIV and influenza evolve faster than the human immune system can contain them, allowing them to circumvent traditional vaccination approaches and causing over one million deaths annually. Agent-based models can be used to simulate the complex interactions that occur between immune cells and hm-IDP-like proteins (antigens) during affinity maturation-the process by which antibodies evolve. Compared to existing experimental approaches, agent-based models offer a safe, low-cost, and rapid route to study the immune response to vaccines spanning a wide range of design variables. However, the highly stochastic nature of affinity maturation and vast sequence space of hm-IDPs render brute force searches intractable for exploring all pertinent vaccine design variables and the subset of immunization protocols encompassed therein. To address this challenge, we employed deep reinforcement learning to drive a recently developed agent-based model of affinity maturation to focus sampling on immunization protocols with greater potential to improve the chosen metrics of protection, namely the broadly neutralizing antibody (bnAb) titers or fraction of bnAbs produced. Using this approach, we were able to coarse-grain a wide range of vaccine design variables and explore the relevant design space. Our work offers new testable insights into how vaccines should be formulated to maximize protective immune responses to hm-IDPs and how they can be minimally tailored to account for major sources of heterogeneity in human immune responses and various socioeconomic factors. Our results indicate that the first 3 to 5 immunizations, depending on the metric of protection, should be specially tailored to achieve a robust protective immune response, but that beyond this point further immunizations require only subtle changes in formulation to sustain a durable bnAb response. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Faris, Orbidan, Wells, Petersen and Sprenger.)
Databáze:	MEDLINE
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