Periodic Reporting for period 1 - Eco-CosmePharm (Computational 'eco-toxicity' assessment of pharmaceutical and cosmetics materials, an approach towards a green and sustainable environment)
Reporting period: 2019-09-16 to 2021-09-15
The main objective is to identify and reduce the impact of potentially hazardous pharmaceuticals, cosmetics, and solvents on the aquatic environment. The toxicity-related properties that will be studied include acute and chronic toxicity, biodegradation, and bioaccumulation. The research methodology to perform toxicity assessment will majorly involve Quantitative Structure-Toxicity Relationship (QSTR), which employs several machine learning approaches for understanding the structural features responsible for aquatic toxicity.
Conclusion of the actions: We have developed a multi-tasking QSTR model to predict the acute and chronic toxicity of pharmaceuticals and cosmetics. Moreover, we have also developed a multi-tasking QSAR model employing random forest technique to predict the bioaccumulation and a classification-based QSAR model employing linear discriminant analysis technique to predict the biodegradation status of chemicals of our interest. The knowledge gained from the QSAR studies helped us in classifying existing marketed pharmaceuticals and cosmetics into toxic and non-toxic groups. Further, we have also performed the experimental validation of the developed models and the results are encouraging. Interestingly, this project also resulted in the development of highly user-friendly AI-based software tools ‘ProtoML-Basic’ and ‘ProtoML-Mixture’ for efficiently executing several QSAR and machine learning tasks. In the near future, the study will surely help us in screening or designing novel analogs of selected toxic chemicals or to identify alternative chemicals that might show similar desirable physicochemical properties with less or no eco-toxicity.
In the second work package, the first task was initially planned to develop new descriptors specific for studying salts, organo-metallic, inorganics, and mixture. However, based on the literature survey we realized that much of the work is already going on with the development of descriptors for the mentioned types of chemicals, and thus we planned to perform a study to evaluate the applicability and efficiency of typically used descriptors as well as new descriptors recommended for handling such chemicals. Thus, as a case study, we have developed a generalized nanoQSAR model for predicting cytotoxicity and genotoxicity of metal oxides nanoparticles (published here DOI: 10.4018/IJQSPR.20201001.oa2). The next task was the development of the QSAR methodology for mixtures. There were two key areas where we can contribute to this topic, i.e. first to compile the information and design a rational methodology, and second to move one step ahead and develop a user-friendly software platform ‘ProtoML-Mixture’ to execute the designed methodology for handling mixtures. To demonstrate the capabilities of the developed software ‘ProtoML-Mixture’ we have performed a case study, where we have developed a multi-tasking QSTR model for predicting the ecotoxicity of deep eutectic solvents (DESs).
In the third work package, we have developed an artificial intelligence (AI) based 'Proto-ML' software to develop and validate the regression and classification-based QSAR models. It has a lot of functionalities that will help users to develop robust models and it works like a workflow, which makes it highly user-friendly. It comprises several essential nodes starting from reading and confirming the input data, data pre-treatment, data set splitting, machine learning (ML) technique node comprising several feature selection methods, ML techniques, as well as, options to set validation parameters, graphs/plots, and applicability domain determination method and finally output data node. In the fourth work package, we have developed several QSTR/QSAR models including multi-tasking that assisted us to screen potential pharmaceuticals and/or cosmetics that may show aquatic toxicity. Thus, first, we have developed requisite QSTR models to predict the acute and chronic toxicity of pharmaceuticals and cosmetics, as well as, to predict the biodegradation and bioaccumulation status. Later, these models were ultimately utilized in screening marketed pharmaceuticals and cosmetics to identify potential chemicals that can be harmful to aquatic life. On screening 8282 chemicals, we have selected 55 chemicals from different toxicity categories for experimental validation of the developed models. The toxicity studies on Daphnia magna were performed in Xenobiotics S.L. (secondment 1), while studies on Oncorhynchus mykiss were performed in INIA, Madrid (secondment 2). We have disseminated the progress and results of the project in about 10 scientific events including conferences, workshops, etc., and promoted in social media.
This project also resulted in highly user-friendly software tools ‘ProtoML-Basic’ and ‘ProtoML-Mixture’ for efficiently executing several QSAR and machine learning tasks. The software tools will be freely available upon registration and we are expecting that these software tools will be very useful for students, researchers, as well as, industry people.