Purpose To develop accurate in silico predictors of Plasma Protein Binding (PPB). Methods Experimental PPB data were compiled for over 1,200 compounds. Two endpoints have been considered: (1) fraction bound (%PPB); and (2) the logarithm of a pseudo binding constant (lnKa) derived from %PPB. The latter metric was employed because it reflects the PPB thermodynamics and the distribution of the transformed data is closer to normal. Quantitative Structure-Activity Relationship (QSAR) models were built with Dragon descriptors and three statistical methods. Results Five-fold external validation procedure resulted in models with the prediction accuracy (R2) of 0.67±0.04 and 0.66±0.04, respectively, and the mean absolute error (MAE) of 15.3±0.2% and 13.6±0.2%, respectively. Models were validated with two external datasets: 173 compounds from DrugBank, and 236 chemicals from the US EPA ToxCast project. Models built with lnKa were significantly more accurate (MAE of 6.2–10.7%) than those built with %PPB (MAE of 11.9–17.6%) for highly bound compounds both for the training and the external sets. Conclusion The pseudo binding constant (lnKa) is more appropriate for characterizing PPB binding than conventional %PPB. Validated QSAR models developed herein can be applied as reliable tools in early drug development and in chemical risk assessment.

The assessment of the combined effects of substances is usually based on concentration addition (CA), independent action (IA) or effect summation (ES) models. Both concepts are founded on different modes of actions of substances, but the knowledge about their relationship is rare. In this paper, we derived a series of inequalities for CA, IA and ES models, and proposed two novel models i.e., ES with the exponent e (ESE) model and ES with the power of the number of components n (ESN) model to evaluate the mixture effect. Our results may have certain significance in mixture risk assessment practices.

Abstract:The predicted toxicity of mixtures of imidazolium and pyridinium ionic liquids (ILs) in the ratios of their EC 50 , EC 10 , and NOEC (no observed effect concentration) were compared to the observed toxicity of these mixtures on luciferase. The toxicities of EC 50 ratio mixture can be effectively predicted by two-stage prediction (TSP) method, but were overestimated by the concentration addition (CA) model and underestimated by the independent action (IA) model. The toxicities of EC 10 ratio mixtures can be basically predicted by TSP and CA, but were underestimated by IA. The toxicities of NOEC ratio mixtures can be predicted by TSP and CA in a certain concentration range, but were underestimated by IA. Our results support the use of TSP as a default approach for predicting the combined effect of different types of ILs at the molecular level. In addition, mixtures of ILs mixed at NOEC and EC 10 could cause significant effects of 64.1% and 97.7%, respectively. Therefore, we should pay high attention to the combined effects in mixture risk assessment.

The inhibition toxicities of four ionic liquids and four heavy metal compounds to MCF-7 human breast cancer cells (MCF-7) and Vibrio qinghaiensis sp.-Q67 (Q67) were determined using MTS assay and microplate toxicity analysis, respectively. The resulting concentration-response data was modeled by using sigmoidal models (Weibull and Logit). Results showed that all concentration -response relationships could be effectively described by the Weibull or Logit function. The toxicities of ILs to both MCF-7 and Q67 have the alkyl chain effect character. However, the toxicity order of four heavy metal compounds to MCF-7 and Q67 were different. Compared with Q67, MCF-7 is more sensitive to heavy metal compounds and less sensitive to ILs

The synergistic effects of mixtures of CdCl2, Ni (NO3)2, CuSO4, and ZnSO4 on photobacterium Q67 were predicted and evaluated by using models of concentration addition (CA), independent action (IA), effect summation (ES), ES with the exponent e (ESE), ES with the power of the number of components n (ESN), and integrated CA with IA based on multiple linear regression (MLR) model (ICIM). The effects of all mixtures were underestimated by CA, IA, ES, ESE, and ESN models, but were effectively predicted by the ICIM model.