Fragrance materials are widely used in cosmetics and other consumer products. The safety assessment of these ingredients includes skin absorption as this is an important parameter in estimating systemic exposure. In the current safety assessment process a compound is assumed as having 100% skin absorption when experimental data are lacking. Hence better estimates for absorption will help provide a better assessment of new fragrances and topically applied chemicals.
A model has been created with reference to published work from Shen et al directed at predicting Jmax (a theoretically achieved dose based on Fick's first law of diffusion) where Jmax = Kp * Cwater on a data set of fragrance-like molecules. The 131 Jmax values are skewed but LogJmax are more normally distributed and so LogJmax modelled in Auto-Modeller using an 80:10:10% split of the data.
The values of LogCwater and LogK(oct/water) from the paper can be modelled successfully with the LogS and LogP models (available within StarDrop and ADME QSAR module). The Kp value in the paper is derived from a consensus of 7 models for LogK(oct/water), this property can be calculated directly. The Shen paper contains experimental and estimated values for Kp and was combined with Kp data derived from Alves et al to give a data set of 202 compounds. This set was split in 70:15:15% to generate a model for LogKp.
The best performing model for LogJmax was generated using the Gaussian Process methodology with rescaled forward variable selection (GP-RFVS).
To be smelt a compound has to have a low enough vapour pressure to be in the gaseous state and have sufficient affinity/efficacy at olfactory receptors. A related measure of volatility is odour threshold.
A data set was collected from the web pages (accessed Jan2018) created by Dr John C. Leffingwell. It details the odour thresholds of various chemical classes where chirality modifies the odour descriptions of the enantiomeric pairs.
The full data set contains over 700 enantiomeric pairs, however, only 422 compounds had suitable data for modelling the odour threshold quantified in ppb. The data range for this set was extremely large, covering more than 10 orders of magnitude and so the log of the ppb data was modelled. Where compounds were listed as "odourless" a value of 7 was used as a default; ppb values of '-3' and '1' were used to classify the set. As the StarDrop descriptors are unaware of chirality the category boundaries were chosen to cope with the changes in threshold which were solely due to changes at the chiral centre(s).
The best model produced was a Random Forest model.
Odour type is an important property for the Flavour industry. It has been observed that compounds with a similar volatility may have similar odour profiles. Kovats Indices (Gas Chromatography peaks) are routinely used within the industry to identify compound volatility.
Data sets comprise 738 odorants for OV101 and 540 odorants for C20M gathered from articles published since 1984. The models were built using StarDrop's Auto-Modeller module with the default settings (split 70:15:15% using clustering) using the StarDrop fingerprint as descriptors. Further details regarding the dataset are available at; http://www.flavornet.org/index.html and data at: http://www.flavornet.org/flavornet.html.
The best performing models for these two chromatographic methods are both Gaussian Process models.
Baell and Holloway published a set of substructure filters for removal of what they termed "Pan Assay Interference Compounds (PAINS)" from screening collections [J. Med. Chem. 2010 53(7) pp. 2719-2740]. These define functional groups that are known to cause problems due to reactivity, poor development potential or known toxicity and have been widely adopted. You can download a model which calculates the number of PAINS matches for each compound here.
The volume of distribution (VDss) is an in vivo pharmacokinetic parameter representing the hypothetical volume into which the dose of drug would have to be evenly distributed to give rise to the same concentration observed in the blood plasma. This provides an indication of the distribution of the drug in the body: A low VDss indicates high water solubility or high plasma protein binding, because more of the drug remains in the plasma; a high VDss suggests significant concentration in the tissues, for example due to tissue binding or high lipid solubility.
Here we describe models of VDss that can be downloaded for use within StarDrop, built with StarDrop's Auto-Modeller and based on data published by Gombar and Hall .
Blood-brain barrier (BBB) penetration is a measure of the ratio between the compound concentration in brain and blood. Good BBB penetration is required for compounds intended for targets in the central nervous system (CNS). Alternatively, for peripheral targets, poor BBB penetration reduces the risk of CNS side effects.
Shen et al. [J. Chem. Inf. Model. 2010,50( 6) pp. 1034-1] published a paper describing the generation and validation of QSAR models of HIA and blood-brain-barrier penetration (BBB). The data sets with which these models were built and validated were provided in the supplementary information to this paper and the HIA data have been used to build the models described herein. Models of the HIA data from this article are described in another article.
Full details of the data, methods, results and use of these models are provided below.
Human Intestinal Absorption (HIA) is a measure of the fraction of orally dosed compound that enters the bloodstream in the hepatic portal vein. Good HIA is a necessary, but not sufficient, requirement for good oral bioavailability. This is because oral bioavailability is a measure of the percentage of dose reaching the systemic circulation, which includes the effect of other processes, most significantly first pass metabolism by the liver.
Shen et al. [J. Chem. Inf. Model. 2010,50( 6) pp. 1034-1] published a paper describing the generation and validation of QSAR models of HIA and blood-brain-barrier penetration (BBB). The data sets with which these models were built and validated were provided in the supplementary information to this paper and the HIA data have been used to build the models described herein. Models of the BBB data from  are described in another article.
Alexey Zakharov of the National Cancer Institute, National Institutes of Health has developed models of stability in Human liver Microsomes (HLM). Alexey presented details of these models and their validation at the American Chemical Society 2012 Fall National Meeting in Philadelphia and his slides are available in an article on this community site. The methods have also been published in Future Med. Chem. (2012) 4(15), pp. 1933–1944. Alexey has kindly shared three of the models built with StarDrop so that all StarDrop users can access them free-of-charge.
P-glycoprotein (P-gp) is an ATP driven efflux pump encoded by the MDR1 gene, capable of transporting a wide spectrum of chemical structures as well as different classes of drugs. Active transport by P-gp can represent a serious hurdle for pharmaceuticals as transport by P-gp has been associated with reduced bioavailability of orally administered drugs and with decreased ability of drug candidates to cross blood-tissue barriers such as the blood-brain barrier. In addition, if a drug is subject to significant P-gp efflux, its distribution, absorption and elimination could be altered by potent P-gp inhibitors leading to drug-drug interactions. Therefore, from the drug discovery and development perspective, knowledge of the transport of drug candidates by P-gp is desirable at an early stage of the drug design process.
Full details of the data, methods, results and use of these models are provided in the detailed information that can be downloaded with the models, below.
These models are based on data sets published by Zdrazil et al. (Mol. Inf. 31(8), pp. 599–609 (2012)) in a review of public domain data from assays of P-gp efflux activity. Two data sets have been used, one classifying compounds as P-pg inhibitors or non-inhibitors, the second containing pEC50 data from a daunorubicin transport assay in MDR CCRF vcr1000 cells.
Permeability across monolayers of the Caco-2 line of human epithelial colorectal adenocarcinoma cells is a common in vitro model used to assess potential of compounds to be absorbed across the human intestine. The apparent permeability (Papp) from the apical to basolateral has been shown to correlate with the in vivo fraction absorbed following oral administration of a drug (Artursson P. and Karlsson J., Biochem. Biophys. Res. Comm.175(3), pp. 880–5, 1991). A criterion commonly applied to select compounds with a higher chance of achieving good oral absorption is a Papp value of 1x10-5 cm/s or higher.
Nordqvist et al. (QSAR & Comb. Sci. 23(5), pp. 303–310, 2004) published experimental Caco-2 Papp values for a set of small molecules, including marketed drugs. We have applied the StarDrop's Auto-Modeller™ to these data to generate models to predict log(Papp). Two data sets were published with this paper, a training set containing 77 compounds and an independent test set of 23 compounds. For consistency, we have used the same sets to train and validate the models generated.
The training set is smaller than we would ideally like for building a global model of a complex property. To mitigate this, we have used only ten general descriptors to build the models, in an effort to avoid over training. The descriptors used were: logP, McGowan's volume, number of hydrogen bond acceptors and donors, flexibility, topological polar surface area, number of aromatic rings, overall charge, negative charge and positive charge. Please see the detailed model output (which can be downloaded below) and the StarDrop Reference Guide for detailed definitions of these descriptors.
We've built and validated QSAR models of seven physicochemical property endpoints, based on data made available by the US Environmental Protection Agency as part of its Toxicity Evaluation Software Tool (T.E.S.T.) package. The models were built with StarDrop's Auto-Modeller module and are available to all StarDrop users free-of-charge.
These two models calculates the number of sp3 carbons and the total number of carbons compound. These are available to enable the calculation of properties such as saturation - the ratio of sp3 carbons to the total number of carbons (Lovering F, Bikker J, Humblet C. Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem 2009, 52:6752–6756.). They suggest that the typical levels of saturation increase from 0.36 in drug discovery through to 0.47 in drugs.
This calculates the number of aromatic rings in a compound. This has been proposed by Ritchie and Macdonald as a characteristic that is inidcative of the developability of a compound (Ritchie and Macdonald (2009), Drug Discov. Today 14 pp. 1011-1020). They suggest that greater than three aromatic rings increases correlates with poorer compound developability and an increased risk of attrition in development.
We've built and validated QSAR models of seven key toxicity endpoints, based on data made available by the US Environmental Protection Agency as part of its Toxicity Evaluation Software Tool (T.E.S.T.) package. The models were built with StarDrop's Auto-Modeller module and are available to all StarDrop users free-of-charge.
In J. Med. Chem. , 2000, 43 (20), pp 3714–3717, Ertl et al. propose the calculation of two polar surface area values, the first reports the PSA for Nitrogen and Oxygen atoms only, the second also includes Sulfur and Phosphorus in the calculation.
This model is based upon the following work published by Pearce et al.
Pearce BC, Sofia MJ, Good AC, Drexler DM, Stock DA. An empirical process for the design of high-throughput screening deck filters. J Chem Inf Model. 2006;46:1060–1068.