Toward Application and Implementation of in Silico Tools and Workflows within Benign by Design Approaches

Abstract

To avoid adverse side effects of chemicals, pharmaceuticals, and their transformation products (TPs) in the environment, substances should be designed to fully mineralize in the environment at their end-of-life while ensuring a degree of stability as needed for their application. These considerations should be implemented at the very beginning of chemical’s and pharmaceutical’s design (Benign by Design, BbD) to meet requirements set by planetary boundaries and upcoming legal frameworks (e.g., “Chemicals Strategy for Sustainability towards a Toxic-Free Environment” by the European Commission (EC)). In silico tools are already being implemented in the drug discovery process and the assessment of chemicals and pharmaceuticals. The advantage of which is avoiding or at least minimizing animal testing and chemical waste due to experimental testing as well as reducing the time to market. However, in the literature, there are just a few examples of how in silico tools could be implemented in the BbD process. Therefore, this study suggests a workflow supporting practitioners designing new environmentally mineralizing chemicals and pharmaceuticals. This would also result in a much faster and less expensive process than starting with repetitive synthesis and subsequent experimental testing to improve the compounds’ properties.

Modelling biodegradability based on OECD 301D data for the design of mineralising ionic liquids

“…(Q)SBR models help make better informed decisions in the design process prior to the synthesis of chemicals, potentially saving time and resources. [26][27][28][29][30][31] Most of the models for biodegradability of noncharged chemicals, e.g. in EPISuite, Vega, MultiCASE and CATALOGIC, use ready biodegradability data measured according to the MITI test (OECD 301C) and predict either a continuous biodegradation rate or a classification into readily biodegradable or not.…”

Systematic Handling of Environmental Fate Data for Model Development─Illustrated for the Case of Biodegradation Half-Life Data

“…5 Experimental determination of these hazard indicators is costly and time-consuming, and the necessary data for a confident hazard assessment are missing for many substances on the market. 6 Computational models that predict environmental hazard indicators are necessary to fill data gaps for existing chemicals and to screen for environmentally safe chemicals during the industrial research and development of new chemicals, 7 ultimately supporting the phase-out of harmful chemicals on the market. However, model training requires reliable and abundant experimental data of sufficient quality, which is scarce, in particular for biodegradation end points.…”

Identification of structure–biodegradability relationships for ionic liquids – clustering of a dataset based on structural similarity

“…21,22 Therefore, ILs should be designed from the very beginning to fully mineralise in the environment after they have fulfilled their function in order to comply with one criterion of many to be safe and sustainable-by-design. [23][24][25] It is crucial that full mineralisation is reached and no metabolites are formed in the environment since there is a risk of metabolites (i.e. transformation products, TPs) being more (eco)toxic than their parent compounds.…”

“…both should mineralise in the environment. 23,28 In order to support the design of mineralising ILs, it is crucial to understand the underlying structure-biodegradability relationships (SBRs) as shown by Lorenz et al 24 General rules of thumb (RoTs) by Boethling et al for designing biodegradable compounds give a first reference point (Table 1). 29 Many experimental studies compared a small number of ILs within an individual series of analogues and their biodegradation data to identify SBRs, sometimes without calling it SBR identification, e.g., studies by Haiß et al, Suk et al, Harjani et al, and Gathergood et al [30][31][32][33] In addition, several studies reviewed experimental data of selected ILs to identify SBRs and to deduce RoT for this specific substance class (Table 1).…”

“…In order to support the design of mineralising ILs, it is crucial to understand the underlying structure–biodegradability relationships (SBRs) as shown by Lorenz et al 24 General rules of thumb (RoTs) by Boethling et al for designing biodegradable compounds give a first reference point (Table 1). 29 Many experimental studies compared a small number of ILs within an individual series of analogues and their biodegradation data to identify SBRs, sometimes without calling it SBR identification, e.g.…”