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Fundamental to regulatory guidelines is to identify chemicals that are implicated with adverse human health effects and inform public health risk assessors about "acceptable ranges" of such environmental exposures (e.g., from consumer products and pesticides). The process is made more difficult when accounting for complex human exposures to multiple environmental chemicals. Herein we propose a new class of nonlinear statistical models for human data that incorporate and evaluate regulatory guideline values into analyses of health effects of exposure to chemical mixtures using so-called 'desirability functions' (DFs). The DFs are incorporated into nonlinear regression models to allow for the simultaneous estimation of points of departure for risk assessment of combinations of individual substances that are parts of chemical mixtures detected in humans. These are, in contrast to published so-called biomonitoring equivalent (BE) values and human biomonitoring (HBM) values that link regulatory guideline values from in vivo studies of single chemicals to internal concentrations monitored in humans. We illustrate the strategy through the analysis of prenatal concentrations of mixtures of 11 chemicals with suspected endocrine disrupting properties and two health effects: birth weight and language delay at 2.5 years. The strategy allows for the creation of a Mixture Desirability Function i.e., MDF, which is a uni-dimensional construct of the set of single chemical DFs; thus, it focuses the resulting inference to a single dimension for a more powerful one degree-of-freedom test of significance. Based on the application of this new method we conclude that the guideline values need to be lower than those for single chemicals when the chemicals are observed in combination to achieve a similar level of protection as was aimed for the individual chemicals. The proposed modeling may thus suggest data-driven uncertainty factors for single chemical risk assessment that takes environmental mixtures into account., (Copyright © 2018. Published by Elsevier Ltd.)

In 2005 the Swedish Association of the Pharmaceutical Industry (LIF) initiated a national environmental classification and information system for pharmaceuticals. This investigation reports the results from a survey, conducted among the persons involved in the start-up process. The aim of this study is to generate knowledge contributing to the clarification of the motivations, expectations, and intentions underlying the development and implementation of the system. The decision to implement a classification and information system for pharmaceuticals was the result of a combination of several driving forces, mainly political pressure and a possibility to increase the industries' goodwill, while at the same time keeping the process under the industries' control. The expected possible effects of the system, other than increased goodwill, are according to this survey assumed to be low. The system offers little guidance for end-users in the substitution of one pharmaceutical for another. One possible reason for this could be that LIF needs to observe the interests of all its members' and should not affect competition. The affiliation of the involved actors correlates to how these actors view and value the system, but this has not hampered the collaborative process to develop and implement it.