G.M. interventions in history . In the past, vaccine design was largely empirical. However, this approach has thus far mostly failed to tackle complex infections such as HIV, (TB), and sp. (malaria), as well as cancers and other noncommunicable diseases. This failure has been attributed to the lack of insight into the underlying mechanisms of how vaccines induce protection (i.e., the rules of immunity) [2,3]. Recent technological advances, including highly multiplexed immune profiling and data-driven computational modeling, have raised the prospect of identifying these rules more globally. Application of systems biology to vaccines [or systems vaccinology (see Glossary)] involves assessing the molecular and cellular state of the immune system before and after vaccination in a comprehensive and unbiased multiomic manner (omics). This is then used to develop data-driven models to predict post-vaccination, pathogen-specific immune responses (e.g., antigen-specific antibody titers); through these, the goal is to identify key molecular immune parameters that correlate with, and potentially shape, vaccine responses. This approach has already led to new insights. For instance, correlation between the early post-vaccination host response and outcome (e.g., PYR-41 antibody responses) has raised the hypothesis that the microbiome may be involved in vaccination responses. Antibiotic-induced shifts in the microbiome can influence responses to influenza virus vaccination in mice and potentially in humans [4., 5., 6.]. This unbiased systems approach is increasingly being applied to vaccine design and testing . This has also led to an increasing appreciation of the extensive baseline and response variability in many immune parameters among individuals within a population . Given the pervasive population heterogeneity, being able to predict who might respond to a given vaccine is necessary. Moreover, understanding how immune status prior to vaccination shapes vaccination responses is important. PYR-41 This has recently been thought to be possible for human influenza virus, hepatitis B virus (HBV), and malaria vaccination [9., 10., 11., 12., 13., 14.,41]. Specifically, the aim would be to assess whether a subjects immune status prior to vaccination allows a predictive response (i.e., the concept of baseline predicts outcome). An important assumption and implication embodied in this hypothesis is that if baseline determines outcome, then altering baseline before vaccination might potentially alter outcome. There is evidence, albeit indirect and preliminary, supporting this hypothesis. The purpose of this Opinion is to highlight the potential of this paradigm. If one can reshape baseline immune status to optimize vaccine PYR-41 responsiveness, it might allow the design of vaccination strategies that can lead to a more effective, safe, and protective immune response (i.e., eliminating nonresponder vaccinees). It may also enable strategies that allow the administration of fewer vaccine doses (ideally only one dose) (Box 1 ). Furthermore, many licensed interventions (e.g., drugs, adjuvants, biologics) known to have immunomodulatory functions might be potentially repurposed to modify baseline status in a targeted fashion. In addition, tools have advanced to allow testing this paradigm in humans, including single-cell large-scale immune profiling and computational modeling; these approaches may contribute to determining, for example, which immune baseline modulators to administer in a tailored fashion and for what types of vaccines. Box 1 Relevance to Precision Health The concept that baseline may predict outcome lends itself to precision public health (i.e., at the population level). This is already done with influenza virus vaccines every year, where different vaccines against influenza virus and/or different numbers of doses of the same vaccine are administered depending on the age of the individual and their prior vaccine status . For example, a 3-year-old child might receive one dose (if previously vaccinated) or two doses (if vaccine na?ve) N-Shc of either a quadrivalent intranasal live-attenuated vaccine or an inactivated vaccine, whereas an individual aged 65 years might receive a single dose of standard quadrivalent inactivated vaccine, a high-dose vaccine, or a formulation that includes the MF59 adjuvant. Alt-text: Box 1 Here, we review evidence supporting the notion that baseline immune status can predict and potentially impact vaccine responses. We hypothesize that iterative application of population-based systems vaccinology studies following PYR-41 the administration of immune modulators and vaccines will help decipher how baseline immune status might impact vaccine outcome. Identification of these predictive parameters and rules is an important step towards reaching the ultimate goal: the ability.