Statistics for Spatial Data.

In this paper, we evaluated the ecotoxicological potential of the 100 pharmaceuticals expected to occur in highest quantities in the wastewater of a general hospital and a psychiatric center in Switzerland. We related the toxicity data to predicted concentrations in different wastewater streams to assess the overall risk potential for different scenarios, including conventional biological pretreatment in the hospital and urine source separation. The concentrations in wastewater were estimated with pharmaceutical usage information provided by the hospitals and literature data on human excretion into feces and urine. Environmental concentrations in the effluents of the exposure scenarios were predicted by estimating dilution in sewers and with literature data on elimination during wastewater treatment. Effect assessment was performed using quantitative structure-activity relationships because experimental ecotoxicity data were only available for less than 20% of the 100 pharmaceuticals with expected highest loads. As many pharmaceuticals are acids or bases, a correction for the speciation was implemented in the toxicity prediction model. The lists of Top-100 pharmaceuticals were distinctly different between the two hospital types with only 37 pharmaceuticals overlapping in both datasets. 31 Pharmaceuticals in the general hospital and 42 pharmaceuticals in the psychiatric center had a risk quotient above 0.01 and thus contributed to the mixture risk quotient. However, together they constituted only 14% (hospital) and 30% (psychiatry) of the load of pharmaceuticals. Hence, medical consumption data alone are insufficient predictors of environmental risk. The risk quotients were dominated by amiodarone, ritonavir, clotrimazole, and diclofenac. Only diclofenac is well researched in ecotoxicology, while amiodarone, ritonavir, and clotrimazole have no or very limited experimental fate or toxicity data available. The presented computational analysis thus helps setting priorities for further testing. Separate treatment of hospital wastewater would reduce the pharmaceutical load of wastewater treatment plants, and the risk from the newly identified priority pharmaceuticals. However, because high-risk pharmaceuticals are excreted mainly with feces, urine source separation is not a viable option for reducing the risk potential from hospital wastewater, while a sorption step could be beneficial.

Environmental toxicology and risk assessment of pharmaceuticals from hospital wastewater

This critical review describes the confused application of significance tests in environmental toxicology and chemistry that often produces incorrect inferences and indefensible regulatory decisions. Following a brief review of statistical testing theory, nine recommendations are put forward. The first is that confidence intervals be used instead of hypothesis tests whenever possible. The remaining recommendations are relevant if hypothesis tests are used. They are as follows: Define and justify Type I and II error rates a priori; set and justify an effect size a priori; do not confuse p(E|H(0)) and p(H(0)|E); design tests permitting Positive Predictive Value estimation; publish negative results; estimate a priori, not post hoc, power; as warranted by study goals, favor null hypotheses that are not conventional nil hypotheses; and avoid definitive inferences from isolated tests.

"What Exactly Are You Inferring?" a Closer Look at Hypothesis Testing [Erratum: 2008 July, v. 27, no. 7, p. 1633.]

Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources.

Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity

On the aquatic toxicity of substituted phenols to Chlorella vulgaris: QSTR with an extended novel data set and interspecies models.

Extensive use of pharmaceuticals as human and veterinary medication raises concerns for their adverse effects on non-target organisms. The purpose of this study was to employ multiple linear regression (MLR) to predict the toxicities of a diverse set of pharmaceuticals to fish. The descriptor pool consisted of about 1500 descriptors calculated using Dragon 5.4, Spartan 06 and Codessa 2.2 software. Descriptor selection was made by the heuristic method available in Codessa 2.2. The data set was divided into training and test sets using Kohonen networks. The training set contained approximately 65% of the compounds of the full data set (99 compounds). The training set model contained eight descriptors from all dimensions, all of which were obtained from Dragon 5.4. The statistical parameters of the model for the training set are R(2 )= 0.664, F = 13.588, and R(cv)(2) (LOO) = 0.542 while it achieves R(2 )= 0.605 for the test set. The training, test and external sets have no response outliers considering the standardized residual greater than three. The external validation of the model was made with a set of pharmaceuticals obtained from several databases. The R(pred)(2) is 0.777, reflecting a relatively good predictive power for the external set.

/pdf/qstr-modelling-of-the-acute-toxicity-of-pharmaceuticals-to-1pr955njjb.pdf

QSTR modelling of the acute toxicity of pharmaceuticals to fish

A multipronged QSAR approach to predict algal low-toxic-effect concentrations of substituted phenols and anilines.

Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets associated with diabetic disease, as well as analysis of protein-ligand binding in order to identify the key binding characteristics of FDs. Therapeutic drug compounds when entering the biological system usually inevitably encounter and interact with a vast variety of biomolecules that are responsible for many different functions in organisms. Protein biomolecules are the most important functional components and used in this study as target structures. The structures of proteins [(PDB ID: 1BMQ, 1FM6, 1GPB, 1H5U, 1US0)] belonging to the class of anti-diabetes targets were obtained from the Protein Data Bank (PDB). Protein binding activity data (binding scores) were calculated for the dataset of 169 FDs related to these five proteins. Subsequently, the resulting data were analyzed using various machine learning and cheminformatics methods, including artificial neural network algorithms for variable selection and property prediction. The Quantitative Structure-Activity Relationship (QSAR) models for prediction of binding scores activity were built up according to five Organization for Economic Co-operation and Development (OECD) principles. All the data obtained can provide important information for further potential use of FDs with different functional groups as promising medical antidiabetic agents. Binding scores activity can be used for ranking of FDs in terms of their inhibitory activity (pharmacological properties) and potential toxicity. 

https://doi.org/10.1016/j.csbj.2022.02.006

Gulcin Tugcu

Papers

On the aquatic toxicity of substituted phenols to Chlorella vulgaris: QSTR with an extended novel data set and interspecies models.

QSTR modelling of the acute toxicity of pharmaceuticals to fish

A multipronged QSAR approach to predict algal low-toxic-effect concentrations of substituted phenols and anilines.

How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases

The integrated use of in silico methods for the hepatotoxicity potential of Piper methysticum