Risk Assessment of Chemical Mixtures in Foods: A Comprehensive Methodological and Regulatory Review
| dc.contributor.author | González Combarros, Rosana | |
| dc.contributor.author | González García, Mariano | |
| dc.contributor.author | Blanco Díaz, Gerardo David | |
| dc.contributor.author | Reino Moya, José Luis | |
| dc.contributor.author | López Sánchez, José Ignacio | |
| dc.date | 2026 | |
| dc.date.accessioned | 2026-10-19T11:43:23Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | Over the last 15 years, mixture risk assessment for food xenobiotics has evolved from conceptual discussions and simple screening tools, such as the Hazard Index (HI), towards operational, component-based and probabilistic frameworks embedded in major food-safety institutions. This review synthesizes methodological and regulatory advances in cumulative risk assessment for dietary “cocktails” of pesticides, contaminants and other xenobiotics, with a specific focus on food-relevant exposure scenarios. At the toxicological level, the field is now anchored in concentration/dose addition as the default model for similarly acting chemicals, supported by extensive experimental evidence that most environmental mixtures behave approximately dose-additively at low effect levels. Building on this paradigm, a portfolio of quantitative metrics has been developed to operationalize component-based mixture assessment: HI as a conservative screening anchor; Relative Potency Factors (RPF) and Toxic Equivalents (TEQ) to express doses within cumulative assessment groups; the Maximum Cumulative Ratio (MCR) to diagnose whether risk is dominated by one or several components; and the combined Margin of Exposure (MOET) as a point-of-departure-based integrator that avoids compounding uncertainty factors. Regulatory frameworks developed by EFSA, the U.S. EPA and FAO/WHO converge on tiered assessment schemes, biologically informed grouping of chemicals and dose addition as the default model for similarly acting substances, while differing in scope, data infrastructure and legal embedding. Implementation in food safety critically depends on robust exposure data streams. Total Diet Studies provide population-level, “as eaten” exposure estimates through harmonized food-list construction, home-style preparation and composite sampling, and are increasingly combined with conventional monitoring. In parallel, human biomonitoring quantifies internal exposure to diet-related xenobiotics such as PFAS, phthalates, bisphenols and mycotoxins, embedding mixture assessment within a dietary-exposome perspective. Across these developments, structured uncertainty analysis and decision-oriented communication have become indispensable. By integrating advances in toxicology, exposure science and regulatory practice, this review outlines a coherent, tiered and uncertainty-aware framework for assessing real-world dietary mixtures of xenobiotics, and identifies priorities for future work, including mechanistically and data-driven grouping strategies, expanded use of physiologically based pharmacokinetic modelling and refined mixture-sensitive indicators to support public-health decision-making. | |
| dc.description.alternative | Durante los últimos quince años, la evaluación del riesgo asociado a mezclas de sustancias químicas presentes en los alimentos ha evolucionado desde métodos sencillos de cribado hacia modelos cuantitativos, probabilísticos y basados en componentes individuales. Este artículo de revisión analiza los avances metodológicos y regulatorios en la evaluación del riesgo acumulativo derivado de la exposición alimentaria a plaguicidas, contaminantes y otros xenobióticos. Se examinan los principales indicadores utilizados, como el índice de peligro (Hazard Index, HI), los factores de potencia relativa (RPF), los equivalentes tóxicos (TEQ), la relación acumulativa máxima (MCR) y el margen de exposición combinado (MOET). Asimismo, se comparan los enfoques regulatorios desarrollados por la Autoridad Europea de Seguridad Alimentaria (EFSA), la Agencia de Protección Ambiental de Estados Unidos (EPA) y la FAO/OMS, con especial atención a los modelos de adición de dosis, la agrupación de sustancias según sus efectos toxicológicos y los procedimientos de evaluación por niveles. El trabajo también analiza la contribución de los estudios de dieta total (TDS) y la biomonitorización humana (HBM) a la estimación de la exposición conjunta. Finalmente, se abordan las principales fuentes de incertidumbre y las necesidades de mejora metodológica, incluyendo el uso de modelos toxicocinéticos y estrategias de agrupación basadas en datos, con el objetivo de facilitar evaluaciones más completas y transparentes del riesgo químico asociado a la alimentación. | |
| dc.identifier.citation | González Combarros, R., González-García, M., Blanco-Díaz, G. D., Segovia Bravo, K., Reino Moya, J. L., & López-Sánchez, J. I. (2026). Risk assessment of chemical mixtures in foods: A comprehensive methodological and regulatory review. Foods, 15(2), Article 244. https://doi.org/10.3390/foods15020244 | |
| dc.identifier.doi | https://doi.org/10.3390/foods15020244 | |
| dc.identifier.issn | 2304-8158 | |
| dc.identifier.uri | https://reunir.unir.net/handle/123456789/23588 | |
| dc.language.iso | eng | |
| dc.publisher | Foods | |
| dc.relation.funding | Universidad Internacional de La Rioja (UNIR) | |
| dc.relation.publisherversion | https://www.mdpi.com/2304-8158/15/2/244 | |
| dc.rights | openAccess | |
| dc.rights.license | CC BY | |
| dc.subject | combined exposure | |
| dc.subject | mixture risk assessment | |
| dc.subject | hazard index (HI) | |
| dc.subject | relative potency factor (RPF) | |
| dc.subject | toxic equivalents (TEQ) | |
| dc.subject | EFSA | |
| dc.subject | Total Diet Studies (TDS) | |
| dc.subject.ocde | 3.00.00 - Ciencias médicas, Ciencias de la salud | |
| dc.subject.ocde | 2.00.00 - Ingeniería, Tecnología | |
| dc.subject.ocde | 4.00.00 - Ciencias agrícolas | |
| dc.title | Risk Assessment of Chemical Mixtures in Foods: A Comprehensive Methodological and Regulatory Review | |
| dc.type | article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| oaire.awardTitle | AGROTOXIA: Prediction of pesticide toxicity in agricultural environments based on Artificial Intelligence models | |
| oaire.awardTitle | Low-cost agro-environmental device for early pest detection | |
| oaire.citation.issue | 2 | |
| oaire.citation.volume | 15 | |
| reunir.tag | ~OPU |


