Characterizing PFAS hazards and risks: a human population-based in vitro cardiotoxicity assessment strategy.

Per- and poly-fluoroalkyl substances (PFAS) are emerging contaminants of concern because of their wide use, persistence, and potential to be hazardous to both humans and the environment. Several PFAS have been designated as substances of concern; however, most PFAS in commerce lack toxicology and exposure data to evaluate their potential hazards and risks. Cardiotoxicity has been identified as a likely human health concern, and cell-based assays are the most sensible approach for screening and prioritization of PFAS.

-A New, Extensible Set of Molecular Substructures for QSAR/QSPR and Read-Across Predictions.

Molecular structure-based predictive models provide a proven alternative to costly and inefficient animal testing. However, due to a lack of interpretability of predictive models built with abstract molecular descriptors they have earned the notoriety of being black boxes. Interpretable models require interpretable descriptors to provide chemistry-backed predictive reasoning and facilitate intelligent molecular design.

Modeling ADMET.

Drug discovery and development is a costly and time-consuming endeavor (Calcoen et al. Nat Rev Drug Discov 14(3):161-162, 2015; The truly staggering cost of inventing new drugs. Forbes.

An evaluation of in-house and off-the-shelf in silico models: implications on guidance for mutagenicity assessment.

The evaluation of impurities for genotoxicity using in silico models are commonplace and have become accepted by regulatory agencies. Recently, the ICH M7 Step 4 guidance was published and requires two complementary models for genotoxicity assessments. Over the last ten years, many companies have developed their own internal genotoxicity models built using both public and in-house chemical structures and bacterial mutagenicity data.

Quantitative structure-activity relationship models of clinical pharmacokinetics: clearance and volume of distribution.

Reliable prediction of two fundamental human pharmacokinetic (PK) parameters, systemic clearance (CL) and apparent volume of distribution (Vd), determine the size and frequency of drug dosing and are at the heart of drug discovery and development. Traditionally, estimated CL and Vd are derived from preclinical in vitro and in vivo absorption, distribution, metabolism, and excretion (ADME) measurements. In this paper, we report quantitative structure-activity relationship (QSAR) models for prediction of systemic CL and steady-state Vd (Vdss) from intravenous (iv) dosing in humans.

Metabolic-intermediate complex formation with cytochrome P450: theoretical studies in elucidating the reaction pathway for the generation of reactive nitroso intermediate.

Mechanism-based inhibition (MBI) of cytochrome P450 (CYP) can lead to drug-drug interactions and often to toxicity. Some aliphatic and aromatic amines can undergo biotransformation reactions to form reactive metabolites such as nitrosoalkanes, leading to MBI of CYPs. It has been proposed that the nitrosoalkanes coordinate with the heme iron, forming metabolic-intermediate complex (MIC), resulting in the quasi-irreversible inhibition of CYPs.

Assessing the toxic effects of ethylene glycol ethers using Quantitative Structure Toxicity Relationship models.

Experimental determination of toxicity profiles consumes a great deal of time, money, and other resources. Consequently, businesses, societies, and regulators strive for reliable alternatives such as Quantitative Structure Toxicity Relationship (QSTR) models to fill gaps in toxicity profiles of compounds of concern to human health. The use of glycol ethers and their health effects have recently attracted the attention of international organizations such as the World Health Organization (WHO).

In silico approaches to predicting cancer potency for risk assessment of genotoxic impurities in drug substances.

The current risk assessment approach for addressing the safety of very small concentrations of genotoxic impurities (GTIs) in drug substances is the threshold of toxicological concern (TTC). The TTC is based on several conservative assumptions because of the uncertainty associated with deriving an excess cancer risk when no carcinogenicity data are available for the impurity. It is a default approach derived from a distribution of carcinogens and does not take into account the properties of a specific chemical.

Predicting P-glycoprotein substrates by a quantitative structure-activity relationship model.

A quantitative structure-activity relationship (QSAR) model has been developed to predict whether a given compound is a P-glycoprotein (Pgp) substrate or not. The training set consisted of 95 compounds classified as substrates or non-substrates based on the results from in vitro monolayer efflux assays. The two-group linear discriminant model uses 27 statistically significant, information-rich structure quantifiers to compute the probability of a given structure to be a Pgp substrate.

Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates.

Computational ADME (absorption, distribution, metabolism, and excretion) models may be used early in the drug discovery process in order to flag drug candidates with potentially problematic ADME profiles. We report the development, validation, and application of quantitative structure-property relationship (QSPR) models of metabolic turnover rate for compounds in human S9 homogenate. Biological data were obtained from uniform bioassays of 631 diverse chemicals proprietary to GlaxoSmithKline (GSK).

Role of ADME characteristics in drug discovery and their in silico evaluation: in silico screening of chemicals for their metabolic stability.

Drug discovery is a long, arduous process broadly grouped into disease target identification, target validation, high-throughput identification of "hits" and "leads", lead optimization, and pre-clinical and clinical evaluation. Each area is a vast discipline in itself. However, all but the first two stages involve, to varying degrees, the characterization of absorption, distribution, metabolism, excretion, (ADME), and toxicity (T) of the molecules being pursued as potential drug candidates.

Comparison of the computer programs DEREK and TOPKAT to predict bacterial mutagenicity. Deductive Estimate of Risk from Existing Knowledge. Toxicity Prediction by Komputer Assisted Technology.

The performance of two computer programs, DEREK and TOPKAT, was examined with regard to predicting the outcome of the Ames bacterial mutagenicity assay. The results of over 400 Ames tests conducted at Glaxo Wellcome (now GlaxoSmithKline) during the last 15 years on a wide variety of chemical classes were compared with the mutagenicity predictions of both computer programs. DEREK was considered concordant with the Ames assay if (i) the Ames assay was negative (not mutagenic) and no structural alerts for mutagenicity were identified or (ii) the Ames assay was positive (mutagenic) and at least one structural alert was identified.

Reliable assessment of logP of compounds of pharmaceutical relevance.

In an earlier study Quant. Struct.-Act.

A quantitative structure-toxicity relationships model for the dermal sensitization guinea pig maximization assay.

We have developed quantitative structure-toxicity relationship (QSTR) models for assessing dermal sensitization using guinea pig maximization test (GPMT) results. The models are derived from 315 carefully evaluated chemicals. There are two models, one for aromatics (excluding one-benzene-ring compounds), and the other for aliphatics and one-benzene-ring compounds.

Assessment of n-octanol/water partition coefficient: when is the assessment reliable?

A model, VLOGP, has been developed for assessment of n-octanol/water partition coefficient, log P, of chemicals from their structures. Unlike group contribution methods, VLOGP is based on linear free energy relationship (LFER) approach and employs information-rich electrotopological structure quantifiers derived solely from molecular topology. VLOGP, a robust and cross-validated model derived from accurately measured experimental log P values of 6675 diverse chemicals, has a coefficient of determination, R2, of 0.

Assessment of effect levels of chemicals from quantitative structure-activity relationship (QSAR) models. I. Chronic lowest-observed-adverse-effect level (LOAEL).

With the multitude of new chemicals being synthesized and the paucity of long-term test data on chemicals that could be introduced into the environment, innovative approaches must be developed to determine the health and environmental effects of chemicals. Research was conducted to employ quantitative structure-activity relationship (QSAR) techniques to study the feasibility of developing models to estimate the noncarcinogenic toxicity of chemicals that are not addressed in the literature by relevant studies. A database of lowest-observed-adverse effect level (LOAEL) was assembled by extracting toxicity information from 104 U.

Assessment of developmental toxicity potential of chemicals by quantitative structure-toxicity relationship models.

Statistically significant quantitative structure-toxicity relationship (QSTR) models have been developed for assessing developmental toxicity potential (DTP) of chemicals. Three submodels, one each for aliphatic, heteroaromatic and carboaromatic compounds, have been cross-validated to ascertain their robustness. The specificities of the models range from 86% to 97%, and their sensitivities between 86% and 89%.

Carcinogenicity of azathioprine: an S-AR investigation.

Besides its use in the treatment of a variety of presumed autoimmune diseases, azathioprine is given as an immunosuppressant to patients who have had renal transplants. Though epidemiological studies have provided "sufficient" evidence of its carcinogenicity in humans, the carcinogenicity tests in rats and mice are considered to be inconclusive because of limitations in the design and results of these tests (IARC, 1981, 1987). Rosenkranz and Klopman (1991) used the CASE program to identify the structural features responsible for its carcinogenicity.

Estimation of maximum tolerated dose for long-term bioassays from acute lethal dose and structure by QSAR.

A quantitative structure-activity relationship (QSAR) model has been developed to estimate maximum tolerated doses (MTD) from structural features of chemicals and the corresponding oral acute lethal doses (LD50) as determined in male rats. The model is based on a set of 269 diverse chemicals which have been tested under the National Cancer Institute/National Toxicology Program (NCI/NTP) protocols. The rat oral LD50 value was the strongest predictor.

Salmonella mutagenicity and rodent carcinogenicity: quantitative structure-activity relationships.

Based on a compilation of 222 reports of rodent nominal lifetime carcinogenicity bioassays by the NCI/NTP on the one hand, and corresponding Salmonella mutagenicity bioassays (Ames tests) on the other, Ashby and Tennant (1988) have divided the carcinogens and non-carcinogens into genotoxic (Ames test positive) and non-genotoxic (Ames test negative) groups and discussed structural characteristics common to each of these groups. The Ames test alone was deemed to be adequate for the identification of genotoxicity because other short-term bioassays, and even combinations, or batteries, appeared to offer no significant advantages. From the results of this study it is possible to achieve (1) a division of the carcinogens into the same genotoxic and non-genotoxic groups, and (2) a division of the non-genotoxic compounds into the same carcinogenic and non-carcinogenic groups, solely on the basis of structure-activity relationships, with a classification accuracy of approx.

Quantitative structure-activity relationships. Fujita-Ban analysis of beta-adrenergic blocking activity of 1-phenoxy-3-([(substituted amido)alkyl]amino)-2-propanols.

Quantitative structure-activity relationship studies have been performed in a series of 81 beta-adrenergic blocking 1-phenoxy-3-([substituted amido)alkyl] amino)-2-propanols using the Fujita-Ban model. A correlation significant at p less than 0.0005 (r = 0.

Structure-activity relationship studies of antiviral N-quinolin-4-yl-N'-benzylidenehydrazine derivatives by discriminant analysis.

The classification technique of linear discriminant analysis (LDA) is applied for studying the structure-activity relationship among antiviral N-quinolin-4-yl-N'-benzylidenehydrazine (II) derivatives. The total hydrophilicity of substituents in the benzylidene moiety along with 4 indicator variables is found to significantly (p less than 0.001) discriminate 25 inactive congeners of (II) from 28 active congeners with more than 80% posterior classification ratio.

Quantitative structure-activity relationships. Antiinflammatory activity of salicylic acid derivatives.

A series of 28 substituted salicylic acids possessing antiinflammatory property is investigated for structure-activity relationships in light of the linear free energy related (LFER) and Fujita-Ban models. The Fujita-Ban group contributions have been calculated for 11 different substituents on the parent ring. It is observed that among these only phenyl group at position 5 increases the activity.

Quantitative structure-activity studies. 2nd Communication: De novo calculations on antilipemic activity of 4-(4-chlorobenzyl)phenoxyacetic acid esters.

Quantitative structure-activity studies are carried out on antilipemic activity of 4-(4-chlorobenzyl)phenoxyacetic acid esters (1). Fujita-Ban group contributions are calculated for a variety of substituents. It is observed that when R1 is pyrid-3-ylmethyl, it has maximum contribution to activity and minimum toxic effects expressed in terms of liver growth.