Should food additives become a priority for quantum computing?

Currently, the approval process for a new food additive by regulatory agencies such as the FDA (U.S. Food and Drug Administration) and ANVISA (Brazil’s National Health Surveillance Agency) follows a rigorous scientific methodology. However, it also faces criticism regarding its speed and scope, especially as societies increasingly demand healthier food with reduced cumulative health risks.

In the case of the FDA, the manufacturer or applicant must submit a detailed technical-scientific dossier demonstrating the safety of the substance for use in food. This document includes toxicological studies, acceptable daily intake (ADI) analyses, population exposure assessments, chemical stability, labeling, and data on metabolism and excretion. In some cases, the GRAS (Generally Recognized As Safe) concept may be applied when qualified experts recognize a substance as safe based on publicly available evidence. Still, even this model requires methodological transparency and allows for review.

Similarly, ANVISA requires toxicological, technological, and, when applicable, nutritional evaluations. The agency mandates that submitted data prove there is no health risk, taking into account both the general population and vulnerable groups. ANVISA also requires that additives be used only in authorized food categories and within established limits. The technical opinion is reviewed by specialized committees, and if approved, the substance is added to Brazil’s positive list of authorized food additives.

From a technical standpoint, these models are still effective. Both processes follow international guidelines established by the Codex Alimentarius (FAO/WHO), ensuring a degree of global standardization. However, a key challenge lies in the fact that these systems were designed with a focus on isolated chemical safety, rather than on long-term cumulative effects. They also fall short in considering genetic variability, diverse microbiomes, or the interaction between multiple additives—factors that are increasingly relevant to truly healthy nutrition.

Furthermore, the fast pace of food innovation and market pressure for new preservatives, colorants, and emulsifiers is not always matched by the speed of regulatory analyses. This mismatch can lead to delays in adopting more natural, effective alternatives, or result in approvals of substances that may later reveal previously unknown adverse effects.

Thus, although the current regulatory system is technically robust and evidence-based, it is often considered partially outdated for the standards of today’s society, which demands nutritional personalization, traceability, sustainability, and long-term safety assessments. This is where innovations such as artificial intelligence, quantum computing, and omics-based analyses (genomics, metabolomics, etc.) can be incorporated into regulatory practices—bringing greater precision, predictability, and food safety. The current challenge is not to discard the existing model but to upgrade it using the technological and epistemological tools of our time.

The increasing presence of food additives in processed products has raised important questions about their long-term effects on the human body. Although many of these additives are approved by regulatory bodies and deemed safe within specific limits, there is a significant scientific gap when it comes to a deep, long-term understanding of how these substances interact with complex biological systems. In this context, quantum computing emerges as a potential ally in the search for more precise data and more reliable predictions about the cumulative impact of additives on the human body. This article argues that, although fields like toxicology, biochemistry, and epidemiology have traditionally led additive research, the incorporation of quantum computing could represent a qualitative leap in the analysis of these compounds.

The molecular complexity of food additives, along with their numerous variations in composition, structure, and chemical behavior, makes them difficult to model accurately with traditional computational tools. Quantum computing, however, can simulate molecular interactions at a subatomic level with far greater detail. This is especially relevant for compounds that behave unstably or variably under different bodily conditions, such as temperature, pH, genetic diversity, and individual microbiota. Quantum simulations can help predict how specific substances bind to cellular receptors, accumulate in tissues, interfere with hormonal processes, or even contribute to genetic mutations over decades.

Additionally, with the rise of machine learning and artificial intelligence systems integrated into biomedical databases, quantum computing could dramatically accelerate the processing of clinical, genomic, and environmental data—refining predictive models of additive toxicity across diverse populations. For example, it is currently difficult to trace how a dye or preservative affects an individual with specific comorbidities over a lifetime. Given quantum computing’s ability to process trillions of variables simultaneously, it will be possible to create more realistic and personalized risk profiles based on molecular evidence, potentially transforming both regulatory policies and food manufacturing processes.

It is worth noting that while other scientific fields—such as pharmacology, epigenetics, and nanotechnology—have contributed advances to our understanding of food additives, they still face limitations in processing speed, model accuracy, and the ability to simulate long-term effects with multiple interacting factors. Quantum computing, when integrated into these disciplines, will not replace their contributions but will significantly enhance them. Rather than testing additives in isolation and over short periods, we will be able to deeply simulate the effects of substance combinations within a complete organism—from absorption and metabolism to excretion or potential bioaccumulation.

Therefore, the notion that food additives should become a priority in quantum computing research is increasingly plausible within the scientific field. Not only because this technology allows for more robust analyses and long-term forecasts, but also because it could contribute to more effective public health policies, the reformulation of less harmful industrial products, and safer, smarter food consumption. The advancement of quantum computing is no longer limited to purely technological domains—it is emerging as a central tool for reshaping scientific understanding at the intersection of food chemistry and human health. The quest for more complete answers about food additives is no longer just a matter of laboratory science—it is a matter of computational capacity. And in that regard, the future is already in motion.