Computational Toxicology for Drug Safety and a Sustainable Environment

Computational toxicology is a rapidly developing field that uses computational logarithms and mathematical models for a better understanding of the toxicity of compounds and test systems. This recent branch is a combination of various fields encompassing chemistry, computer science, biology, biochemistry, mathematics, and engineering. This chapter focuses on the usage of computational toxicology in various fields. This multifaceted field finds application in almost every pharmaceutical and industrial process which in turn offers safer environmental practices. Computational toxicology has revolutionized the field of drug discovery as it has helped in the production of significantly efficient drug molecules through time-saving and cost-effective methods. It has also proved a boon for various industries ranging from often-used cosmetics to daily-use food products, as toxicological assessment of chemical constituents in them provides quicker and safer production. All these computational assessments thereby save a lot of chemical wastage and thus give a helping hand in exercising healthy environmental practices. Besides this, pollutant categorization and waste management through computational tools have also been favoured by many agencies that work for environmental sustainability. Thus, to sum up, computational technology has completely transformed the processes and practices followed in pharmaceutics, environment protection and industries, and paved the way for efficient, cost-effective, and less hazardous routes. 

Complex computational models of biological systems are developed to simulate and emulate various biological systems, but many times, these models are subjected to doubt due to inconsistent model verification and validation. The verification and validation of a model are important aspects of model construction. Moreover, the techniques used to perform the verification and validation are also important as the improper selection of the verification and validation techniques can lead to false conclusions with profound negative effects, especially when the model is applied in healthcare. The objective of this chapter is to discuss the current verification and validation techniques used in the analysis and interpretation of biological models. This chapter aims to increase the efficiency and the peer acceptability of the biological prediction models by encouraging researchers to adopt verification and validation processes during biological model construction. 

One of the chief reasons for drug attrition and failure to become a marketed drug is the potential toxicity associated with its administration. Therefore, many drugs encountered in the past reached the last phase of drug development successfully but could not be marketed despite their potential drug-likeness due to their inevitable toxicity properties. This issue can be addressed considerably by employing computational toxicological approaches for predicting the toxicity parameters of a drug candidate before its practical synthesis. Pharmaceutical companies utilise computer-based toxicity predictions at the design stage for identifying lead compounds possessing the least toxic properties, and also at the optimization stage for selecting candidates as potential drugs. This integrative field has been exploited for various applications including hazard and risk prioritization of chemicals and safety screening of drug metabolites. The importance of QSTR models for the computational prediction of toxicity is also discussed in this chapter. Various important and predominant software for in silico toxicity prediction including ADMETox, OSIRIS Property Explorer, TopKat and admetSAR 2.0 are also covered herein. This chapter also discusses various freely accessible online clinical repositories such as BindingDB, PubChem, ChEMBL, DrugBank and ChemNavigator iResearch Library. Therefore, the present chapter focuses on the role played by computational toxicology in the procedure of drug profiling and in establishing freely accessible online clinical repositories.

Computational toxicology is an applied science that combines the use of the most recent developments in biology, chemistry, computer technology, and mathematics. Integrating all of these fields into a biologically based computer model to better understand and anticipate the negative health impacts of substances like environmental contaminants and medications. As public demand rises to eliminate animal testing while maintaining public safety from chemical exposure, computational approaches have the potential of being both rapid and inexpensive to operate, with the ability to process thousands of chemical structures in a short amount of time. The agency's computational toxicology lab is always working on new models for decisionsupport tools such as physiologically based pharmacokinetic (PBPK) models, benchmark dose (BMD) models, computational fluid dynamics (CFD) models, and quantitative structure-activity relationship (QSAR) models. The models are being used to analyze the toxicological effects of chemicals on mammals and the environment in a variety of industries, including cosmetics, foods, industrial chemicals, and medicines. Additionally, the toolbox’s understanding of toxicity pathways will be immediately applicable to the study of biological responses at a variety of dosage levels, including those more likely to be typical of human exposures. The uses of computational toxicology in environmental, pharmacological, and industrial processes are covered in this study.

Pharmaceuticals are necessary products that have indubitable benefits for people's health and way of life. Following their use, there is a corresponding increase in the production of pharmaceutical waste. We need to figure out how to lessen the production of pharmaceutical waste and prevent its release into the environment, which could eventually pose major health risks to the rest of the living world. If handled incorrectly, pharmaceutical waste increases the danger, which is inversely correlated with the active concentration of chemical components in various environmental compartments. As a result, when drugs and their unaltered metabolites are dispersed into the environment through several sources and channels, they may influence both animals and humans. Finding the sources and points of entry of pharmaceutical waste into the ecosystem is the first step in understanding pharmaceutical ecotoxicity. Several techniques, like the Structure-Activity Relationship (SAR) and Quantitative StructureActivity Relationship (QSAR) models, help assess and manage environmental risks caused by pharmaceutical waste. The persistency, mobility, and toxicity (PMT) of pharmaceutical compounds have been predicted computationally using QSAR models from OPERA QSAR, VEGA QSAR, the EPI Suite, the ECOSAR, and the QSAR toolbox. In silico predictions have been made for molecular weight, STP total removal, sewage treatment plant, Octanol-water partition coefficient (KOW), ready biodegradability, soil organic adsorption coefficient, short- and long-term ecological assessments, carcinogenicity, mutagenicity, estrogen receptor binding, and Cramer decision tree. The adverse effects of medications on the living world, as well as risk assessment and management, have been covered in this chapter. Several computational methods that are employed to counteract the negative consequences of pharmaceutical waste have also been addressed. The goal is to better understand how to minimize the concentration of pharmaceutical waste in our environment. 

The paper and pulp industry generates enormous amounts of wastewater containing high quantities of chlorinated toxicants. These volatile organochlorine compounds are widespread toxic chemicals that may cause harmful effects on humans via interaction with human α-amino-β-carboxymuconate-ε-semialdehyde decarboxylase (hACMSD) which is a vital enzyme of the kynurenine pathway in tryptophan metabolism. It averts the accumulation of quinolinic acid (QA) and supports the maintenance of the basal Trp-niacin ratio. Herein, we report the optimization of organochlorine compounds employing density functional theory (DFT) with B3LYP/6- 311G+(d,p) basis set to elucidate their frontier molecular orbitals as well as the chemical reactivity descriptors. The DFT outcome revealed that organochlorine compounds show a lower HOMO-LUMO gap as well as a higher electrophilicity index and basicity as compared to the substrate analogue, Dipicolinic acid. To assess the structure-based inhibitory action of organochlorine compounds, these were docked into the active site cavity of hACMSD. The docking simulation studies predicted that organochlorine compounds require lower binding energy (-3.86 to -6.42 kcal/mol) which is in good agreement with the DFT calculations and might serve as potent inhibitors to hACMSD comparable with its substrate analogue, Dipicolinic acid which has a binding affinity of -4.41 kcal/mol. Organochlorine compounds interact with key residues such as Arg47 and Trp191 and lie within the active site of hACMSD. The high binding affinity of organochlorine compounds was attributed to the presence of several chlorine atoms, important for hydrophobic interactions between the organochlorine compounds and the critical amino acid residues of the receptor (hACMSD). The results emphasized that organochlorine compounds can structurally mimic the binding pattern of Dipicolinic acid to hACMSD. 

Toxicology is a domain imbricating biology, chemistry, pharmacology, and medicine that involves observing and analyzing inauspicious consequences of chemical exposure on living beings thus identifying and manifesting toxins and toxicants. Progress in computer sciences and hardware in combination with equally remarkable growth in molecular biology and chemistry are providing toxicology with a reigning new tool case. This tool case of computational models assures to enhance the efficacy by which the hazards and risks of environmental chemicals are driven. In this study, we investigated two compounds namely: Phenylgloxylic acid (PGA) and 4-ethynyl anisole (MOPA) experimentally as well as quantum chemically. Density functional theory was employed to investigate the tilted compounds theoretically. All the Quantum chemical calculations were performed by implying the Density functional theory technique, B3LYP method and 6-311++G (d, p) basis set. The reactive areas of the molecule were obtained by Fukui functions. The ADME properties and drug-likeness nature of the derivatives were obtained by SwissADME Tool [1]. Molecular docking studies were also performed with different receptor proteins to study the best ligand-protein interactions. The biological study-drug-likeness was also performed to check the druglike nature of the molecule. 

Customary firework burning during different festivals and occasions have been reported from different parts of the world. The pollutants emitted from fireworks exert toxicological effects on human health and the environment. A virtual study was performed to assess the extent of binding of sixteen important components of fireworks including Al2O3 , Ba(NO3 )2 , C6H6 , CO, Ethylbenzene (C8H10) Fe2O3 .H2O, KClO3 , KClO4 , KNO3 , Na2C2O4, NH3 , NO, o-Xylene (C8H10), SO2 , Sr(NO3 )2 and Toluene (C7H8 ) with human superoxide dismutase (SOD), human serum albumin (HSA), and estrogen related receptor gamma (ERR-gamma) proteins. AutoDock 4.2.6 was employed to perform rigid docking. Against HSA, NH3 exhibited the least binding energy i.e. -5.19 kcal/mol. Against ERR-gamma, Al2O3 showed the least binding energy i.e., -4.08 kcal/mol. With SOD, ethylbenzene exhibited binding energy of -4.62 kcal/mol. A molecular dynamics simulation of 10 ns was performed on the ERR-gamma-o-xylene complex at 300K at the molecular mechanics level using GROMACS 5.1.2., showing conformational changes within the protein due to the o-xylene binding. The average Root Mean Square Fluctuation of the complex was 0.0821 nm. The results can be further elaborated and may guide future research for the intervention of protein targets for chemical toxins.

The trial on non-testing approaches for nanostructured materials and the prediction of toxicity that may cause cell disruption is needed for the risk assessment, to recognize, evaluate, and categorize possible risks. Another tactic for examining the toxicologic characteristics of a nanostructure is using in silico methods that interpret how nano-specific structures correlate to noxiousness and permit its prediction. Nanotoxicology is the study of the toxicity of nanostructures and has been broadly functional in medical research to predict the toxicity in numerous biotic systems. Exploring biotic systems through in vivo and in vitro approaches is affluent and time-consuming. However, computational toxicology is a multi-discipline ground that operates In silico strategies and algorithms to inspect the toxicology of biotic systems and also has gained attention for many years. Molecular dynamics (MD) simulations of biomolecules such as proteins and deoxyribonucleic acid (DNA) are prevalent for considering connections between biotic systems and chemicals in computational toxicology. This chapter summarizes the works predicting nanotoxicological endpoints using (ML) machine learning models. Instead of looking for mechanistic clarifications, the chapter plots the ways that are followed, linking biotic features concerning exposure to nanostructure materials, their physicochemical features, and the commonly predicted conclusions. The outcomes and conclusions obtained from the research, and review papers from indexing databases like SCOPUS, Web of Science, and PubMed were studied and included in the chapter. The chapter maps current models developed precisely for nanostructures to recognize the threat potential upon precise exposure circumstances. The authors have provided computational nano-toxicological effects with the collective vision of applied machine learning tools.