Physico-Chemical Property prediction of emerging pollutants:PFCs and (B)TAZs for environmental distribution

Perfluorinated compounds (PFCs) and (Benzo)triazoles (B/TAZs) are considered as “emerging pollutants” as they are broadly distributed in the environment, because of their extensive use and are considered to be hazardous, as they cause adverse effects to humans and other non-target species. The lack of physico-chemical properties of these pollutants urges the use of available limited data to predict such properties for other existing or novel chemicals, as suggested by REACH. Internally robust and externally validated QSPR models were developed for these compounds. For PFCs, QSPR models on Water Solubility (WS), Vapor Pressure (VP) and Critical Micelle Concentration (CMC) were developed and structural applicability domain (AD) was verified. 79% of PFCs in ECHA list were found within the AD of all three models. In addition, the relationships between the modeled end-points and Bioconcentration Factor (BCF) were studied. The increasing trend of BCFs is in opposite direction to that of WS and CMC and it is found different, by Principal Component Analysis (PCA), for carboxylates and sulfonates. For B/TAZs, four QSPR models on WS, VP, KOW (Octanol/Water partition) and Melting Point (MP) were developed. 66 of 351 studied compounds were found within the structural AD of all four models. These compounds were studied in multivariate plot by PCA to understand their leaching and volatility behavior. Comparison with soil sorption partition coefficient (KOC) was performed by using predictions from earlier published models. More soluble, volatile and sorbed chemicals are highlighted. The 1H-B/TAZs were found to be among the more soluble and less sorbed compounds

Oral LD50 Toxicity Modeling and Prediction of Per- and Polyfluorinated Chemicals on Rat and Mouse

2Quantitative structure-activity relationship (QSAR) analyses were performed using the LD50 oral toxicitdata of per- and polyfluorinated chemicals (PFCs) on rodents: rat and mouse. PFCs are studied under the EU projectCADASTER which uses the available experimental data for prediction and prioritization of toxic chemicals for risk assessment by using the in silico tools. The methodology presented here applies chemometrical analysis on the existing experimental data and predicts the toxicity of new compounds. QSAR analyses were performed on the available 58 mouse and 50 rat LD50 oral data using multiple linear regression (MLR) based on theoretical molecular descriptors selected by genetic algorithm (GA). Training and prediction sets were prepared a priori from available experimental datasets in terms of structure and response. These sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the models were verified on 376 perand polyfluorinated chemicals including those in REACH preregistration list. The rat and mouse endpoints were predicted by each model for the studied compounds, and finally 30 compounds, all perfluorinated, were prioritized as most important for experimental toxicity analysis under the project. In addition, cumulative study on compounds within the AD of all four models, including two earlier published models on LC50 rodent analysis was studied and the cumulative toxicity trend was observed using principal component analysis (PCA). The similarities and the differences observed in terms of descriptors and chemical/mechanistic meaning encoded by descriptors to prioritize the most toxic compounds are highlighted.noneBarun Bhatarai; Paola GramaticaBarun, Bhatarai; Gramatica, Paol

Are mechanistic and statistical QSAR approaches really different? MLR studies on 158 cycloalkyl-pyranones

Two parallel approaches for quantitative structure-activity relationships (QSAR) are predominant in literature, one guided by mechanistic methods (including read-across) and another by the use of statistical methods. To bridge the gap between these two approaches and to verify their main differences, a comparative study of mechanistically relevant and statistically relevant QSAR models, developed on a case study of 158 cycloalkyl-pyranones, biologically active on inhibition (Ki) of HIV protease, was performed. Firstly, Multiple Linear Regression (MLR) based models were developed starting from a limited amount of molecular descriptors which were widely proven to have mechanistic interpretation. Then robust and predictive MLR models were developed on the same set using two different statistical approaches unbiased of input descriptors. Development of models based on Statistical I method was guided by stepwise addition of descriptors while Genetic Algorithm based selection of descriptors was used for the Statistical II. Internal validation, the standard error of the estimate, and Fisher\u2019s significance test were performed for both the statistical models. In addition, external validation was performed for Statistical II model, and Applicability Domain was verified as normally practiced in this approach. The relationships between the activity and the important descriptors selected in all the models were analyzed and compared. It is concluded that, despite the different type and number of input descriptors, and the applied descriptor selection tools or the algorithms used for developing the final model, the mechanistical and statistical approach are comparable to each other in terms of quality and also for mechanistic interpretability of modeling descriptors. Agreement can be observed between these two approaches and the better result could be a consensus prediction from both the models

Opportunities and Challenges Using Artificial Intelligence (AI) in ADME/Tox

A recent conference entitled Artificial Intelligence (AI) Applications in Biopharma Summit meeting organized a panel of scientists who work at the interface of machine learning and absorption, distribution, metabolism, excretion, and toxicology (ADME/Tox). This group represented small and big pharma companies with a combined total of 80 years of experience in the field. The questions generated and the discussion topic was felt to be of broader interest. With the recent rebirth of AI related to pharma, it is timely to present this collaborative commentary to capture the diverging opinions on the past present and future role of AI for ADME/Tox

Divergent allosteric control of the IRE1α endoribonuclease using kinase inhibitors.

Under endoplasmic reticulum stress, unfolded protein accumulation leads to activation of the endoplasmic reticulum transmembrane kinase/endoRNase (RNase) IRE1α. IRE1α oligomerizes, autophosphorylates and initiates splicing of XBP1 mRNA, thus triggering the unfolded protein response (UPR). Here we show that IRE1αs kinase-controlled RNase can be regulated in two distinct modes with kinase inhibitors: one class of ligands occupies IRE1αs kinase ATP-binding site to activate RNase-mediated XBP1 mRNA splicing even without upstream endoplasmic reticulum stress, whereas a second class can inhibit the RNase through the same ATP-binding site, even under endoplasmic reticulum stress. Thus, alternative kinase conformations stabilized by distinct classes of ATP-competitive inhibitors can cause allosteric switching of IRE1αs RNase--either on or off. As dysregulation of the UPR has been implicated in a variety of cell degenerative and neoplastic disorders, small-molecule control over IRE1α should advance efforts to understand the UPRs role in pathophysiology and to develop drugs for endoplasmic reticulum stress-related diseases