No early drug candidate screening tool should neglect toxicity aspects. Living up to its name, ADMET Predictor™ features a rapidly growing array of predictive toxicity models. Individual models are described below.

Cardiac Toxicity - Affinity towards hERG-Encoded Potassium Channels

ADMET Predictor assesses each compound for inhibitory affinity towards hERG-encoded K⁺ channels, which are responsible for the normal repolarization of the cardiac action potential. Blockage or any other impairment of these channels in the heart cells can lead to fatal cardiac toxicity. The TOX_hERG affinity model was trained on carefully selected literature pIC50 values for inhibition of hERG K⁺ channels. Only the most reliable values measured in mammalian cells (human embryonic kidney [HEK], Chinese hamster ovary [CHO], and cardiac myocytes) for the known drugs and drug-like compounds were chosen.


ADMET Predictor TOX_hERG Model Validation

Human Liver Adverse Effects

The US Food and Drug Administration's Center for Drug Evaluation and Research has been collecting reports on human liver adverse effects of drugs since 1968. The result of this work is two databases, Spontaneous Reporting System (SRS) and Adverse Event Reporting System (AERS). ADMET Predictor uses a subset of 490 compounds from publicly available (non-proprietary) SRS database, gathered 1978 - 1996, to model hepatotoxicity of many popular pharmaceuticals. The reports were collected for up to a five year period for each compound in the study.
Modeling Adverse Drug Reaction (ADR) effect is a challenging task because of the uniqueness of this type of data. The number of ADR reports for individual drugs varies and is dependent on the length of time the drug was marketed and the number of patients taking the medication. Some effects are underestimated due to insufficient data. Furthermore, several medications taken simultaneously by a patient make it sometimes difficult to identify a drug mostly attributed to the adverse effect.
To make the data more comparable each data point in the model database accounts for the volume of the pharmaceutical product in the form of a shipping unit. The ADR report data and the pharmaceutical shipping values are used to estimate pharmaceutical usage and human exposure by calculating the Reporting Index (RI = (# ADR reports / # shipping units) × 1,000,000).
The SRS dataset differentiates between 3 classes of compounds: active (RI < 3.0), marginally active (3.0 < RI < 4.0) and inactive (RI > 4.0). The neural network ensemble models employed in ADMET Predictor treat marginally active (marginally toxic) compounds as active (toxic) by setting the RI cutoff value to 3.0. Therefore, molecules with RI < 3.0 are classified as inactive (nontoxic) and those with RI > 3.0 as active. ADMET Predictor offers 5 individual models corresponding to individual enzymes used in hepatotoxicity diagnostics:

1. Alkaline Phosphatase increase
2. SGOT increase
3. SGPT increase
4. LDH increase
5. GGT increase.

Chronic Carcinogenicity and Mutagenicity

ADMET Predictor’s chronic carcinogenicity and mutagenicity models are built using data from the Carcinogenic Potency Database (CPDB).  The CPDB is a curated archive of compound names and tumorigenesis data that is available through the Environmental Protection Agency’s DSSTox program.  As noted by the DSSTox program staff, the CPDB “includes detailed results and analyses of more than 5000 chronic, long term carcinogenesis bioassays reported in over 1200 papers in the general literature and more than 400 Technical Reports of the National Cancer Institute/National Toxicology Program”.

Two quantitative carcinogenicity models based on this data are available in ADMET Predictor.  The first of these, TOX_BRM_Rat, predicts the TD50 value of a particular compound in units of mg/kg/day.  The TD50 is the dose of a substance administered orally to rats over the course of their lifetimes that results in the appearance of tumors in 50 percent of their population.  Likewise, the second carcinogenicity model, TOX_BRM_Mouse, predicts the TD50 value in mice (same units).


ADMET Predictor 2D and 3D TOX_BRM (Carcinogenicity in Mice) Model Validation

The previous generic qualitative model of whether a compound is positive or negative for inducing mutations in cultures of Salmonella bacteria, called TOX_BRM_Sal, has now been replaced by a much more detailed series of 10 models assessing Ames mutagenicity in individual strains of Salmonella. The Ames mutagenicity is a measurement of the mutagenic potential of chemical compounds developed by Bruce Ames and his group with the use of strains of the Salmonella Typhimurium rather than using rodents, the latter taking longer time and costing more. The capability to predict the mutagenicity of compounds is important in drug discovery and development. 

The ten TOX_MUT* Artificial Neural Network Ensembles are qualitative models, predicting the mutagenicity of new compounds as “Active” or “Inactive”.  We also created an ADMET Risk rule file called “TOX_MUT.ro5” which predicts overall mutagenicity by counting “Actives”.


ADMET Predictor TOX_MUT_* (Mutagenicity in Salmonella) Model Validation

Maximum Recommended Therapeutic Dose

Towards the goal of better understanding the relationship between structure, toxicity, and no-effect level (NOEL), the US Food and Drug Administration’s Center for Drug Evaluation and Research has compiled a database of maximum recommended therapeutic dose (MRTD).  The details of the work and the non-proprietary part of the database were published by the Informatics and Computational Safety Analysis Staff (ICSAS) under the authorship of Contrera, et al. (Matthews; 2004).

ADMET Predictor employs neural net ensemble models to predict the MRTD value for both 2D and 3D representations of molecules.  The units of the result are in mg/kg of body weight/day.  Interpretive cutoff value for the model, 3.16 mg/kg/day is approximately equal to the log-mean of values used by Contrera et al.  Predictions lower than 3.16 mg/kg-bw/day indicate an “active” compound with significant potential for side effects.  Predictions higher than 3.16 mg/kg-bw/day indicate an “inactive” compound for which side effects are less likely.


ADMET Predictor TOX_MRTD Model Validation

Acute Rat Toxicity

The acute rat toxicity model is based on the amount of orally administered chemical in mg/kg body weight that produced lethality in 50% of the rats in each respective study regardless of the mode of action. Such a diverse data set poses, therefore, an extreme challenge to a QSTR modeler. Data for this study comes from two sources, the highly overlapping RTECS, Registry of Toxic Effects of Chemical Substances, data set (version previously owned by the Center for Disease Control's National Institute for Occupational Safety and Health) and the ChemIDplus database. 7150 unique identifiable compounds were selected and used to model the endpoint pLD50. In both 2D and 3D models greater than or equal to 20% of the data were set aside for the external test sets prior to training the models.


ADMET Predictor 2D TOX_RAT Model Validation

Allergenic Skin Sensitization

A skin sensitizer is a compound or substance that induces cutaneous allergic reactions. During the past decade the murine local lymph node assay (LLNA) has proven to be a useful tool in assessing the relative potency of compounds as skin sensitizers for risk assessment purposes and it has been recently recommended as a validated method for the determination of the relative potency of skin sensitizing chemicals. Details regarding LLNA standards and applicability domain can be found on the National Toxicology Program's website. The endpoint of the LLNA is the EC3, the estimated concentration of a chemical required to produce a 3-fold stimulation of draining lymph node cell proliferation in mice compared with concurrent controls is used to divide compounds into classes of sensitizers and non-sensitizers. Compounds with an EC3 less than or equal to 10% are considered sensitizers and those greater than 10% are non-sensitizers.As literature data was significantly skewed (80%) toward sensitizing compounds (Gerbereck; 2005 and Roberts; 2007), several known drugs and other compounds known not to have any issues with cutaneous or other allergies were included to balance the data set. In its entirety 298 compounds were used to build and validate both 2D and 3D models. 20% and 30% of the data was set aside as an external test set for the 2D and 3D models, respectively.


ADMET Predictor 2D and 3D TOX_SKIN Model Validation

Estrogen Receptor Binding Toxicity

Numerous studies have been coordinated by the US Food and Drug Administration’s National Center for Toxicological Research in an effort to characterize a wide variety of natural and synthetic estrogens (Fang; 2001, Blair; 2000, Branham; 2002).  A compilation of this data has been placed in the US Environmental Protection Agency’s DSSTox database and used to train predictive models of rat estrogen receptor binding for ADMET Predictor.

Two neural network ensemble models are used to assess a compound’s likelihood of binding to the estrogen receptor.  The first is a straightforward prediction of whether the molecule will have a detectable affinity for the receptor at all.  The second model predicts the degree of binding for those compounds that are identified by the filter as “Toxic” or “Undecided”.  This model displays the relative binding affinity (RBA) of a molecule.  The RBA is a dimensionless number that represents the ratio of the EC50 for estradiol over the predicted EC50 for the drug in question.  Higher values indicate greater binding affinity and likelihood for endocrine-related toxicity.


ADMET Predictor TOX_ER_filter Model Validation


ADMET Predictor 3D TOX_ER Model Validation

Models of Environmental Toxicity

The bioconcentration factor, BCF, has been defined as the ratio of the chemical concentration in biota to that in water at steady state, as a result of absorption via the respiratory surface (Hamelink; 1977). Environmentally, the BCF describes the accumulation of pollutants partitioning from the aqueous phase into an organic phase (typically fish) and does not include uptake due to diet. The BCF has no units, as seen by the equation below:

BCF = [Concentration in organism] / [Concentration in environment]

Here, the BCF and steady-state BCF are synonymous with steady-state occurring after the organism has been exposed for a sufficient length of time such that the ratio does not change substantially. Environmentally, there may be concern if a significant amount of a substance is concentrated in a local environment (through dumping, spillage, production, etc.) and the BCF is significantly greater than 1. The European Union's REACH framework (Registration, Evaluation and Authorization of Chemicals) indicates BCF measurements for pollutant/chemical production or import of greater than 100 tons per year, as the BCF can be useful for classification and labeling, prioritization, and safety assessment purposes. In particular it has been suggested that the BCF could be used in first tier risk assessment of secondary poisoning in wildlife and humans though dietary exposure. The Organization for Economic Co-operation and Development (OECD) 305 guideline specifies the preferred experimental conditions for BCF testing. The number of fish suggested for the test ranges from 132 to 240 with each test being performed from 44 to 116 days. A literature data set of 592 substances with experimentally measured data points was compiled and logBCF was modeled using ANNE methodology. 20% of the entire data set was set aside for the test set and in both 2D and 3D cases 474 molecules were used to train/verify the models.


ADMET Predictor 2D and 3D TOX_BCF Model Validation

Beginning in 1995, the Mid-Continent Ecology Division of the US Environmental Protection Agency tested a set of industrial compounds for lethal effects on Pimephales promelas, the fathead minnow.  The resulting database was used for internal efforts to develop a structure-activity relationship model and was also made available for public use under the EPA’s DSSTox program.  This published data was the basis for training ADMET Predictor’s fathead minnow acute toxicity model, called TOX_FHM.

The result that appears in the TOX_FHM column is the predicted concentration in units of mg/L of a given compound that will kill 50 percent of a population of minnows after an exposure time of 96 hours. 

Although relatively few pharmaceutical compounds appeared in the training set of this model, those molecules that do fall into its chemical space still have a high predictive confidence.  The model is best suited to aromatic, amine-rich, halogenated, or non-polar compounds.


ADMET Predictor 2D and 3D TOX_FHM Model Validation

Another toxicity assay has been developed in the College of Veterinary Medicine at the University of Tennessee in the laboratory of Prof. T.W. Schultz (Schultz 1997).  The assay measures the concentration of toxicant needed to inhibit 50% growth (IGC50) in the protozoan species, Tetrahymena Pyriformis, after approximately 40 hours exposure (8-9 cell cycles in the control group) at 27°C.  Publicly available data from this assay was employed in a recent publication to assess various QSAR modeling approaches by different research groups (Zhu et al, 2008).  In that study, the dataset was partitioned into a training set (provided to each of the modeling groups) and two test sets (the second test set being discovered after the study was initiated).  Using the same partitioning, we matched the best level of performance (in both test sets) as reported among individual models of that study.  By repartitioning the full dataset into training/verification and test sets using a Kohonen map, further improvement in performance was achieved as shown in figure below.


ADMET Predictor 2D and 3D TOX_ATTP Model Validation

References

Blair RM, Fang H, Branham WS, Hass BS, Dial SL, Moland CL, Tong W, Shi L, Perkins RG, and Sheehan DM. "The estrogen receptor relative binding affinities of 188 natural and xenochemicals: Structural diversity of ligands." Toxicol Sci. 2000; 54:138-153.

Branham WS, Dial SL, Moland CL, Hass BS, Blair RM, Fang H, Shi L, Tong W, Perkins RG, and Sheehan DM. "Binding of phytoestrogens and mycoestrogens to the rat uterine estrogen receptor." J Nutr. 2002; 134:658-664.

Fang H, Tong W, Shi LM, Blair R, Perkins R, Branham W, Hass BS, Xie Q, Dial SL, Moland CL, and Sheehan DM. "Structure-activity relationships for a large diverse set of natural, synthetic, and environmental estrogens." Chem Res Tox. 2001; 14:280-294.

Hamelink JL. "Current bioconcentration test methods and theory." in Aquatic Toxicology and Hazard Evaluation. Eds. Mayer FL and Hamelink JL. West Conshohocken, PA ASTM STP, 1977.

Matthews EJ, Kruhlak NL, Benz RD and Contrera JF "Assessment of the health effects of chemicals in humans: I. QSAR estimation of the maximum recommended therapeutic dose (MRTD) and no effect level (NOEL) of organic chemicals based on clinical trial data." Current Drug Discovery Technologies 2004; 1:1

Schultz, TW, "TETRATOX: Tetrahymena pyriformis population growth impairment endpoint - A surrogate for fish lethality." Toxicol Methods. 1997; 7:289-309; http://www.vet.utk.edu/TETRATOX/

Zhu H, Tropsha A, Fourches D, Varnek A, Papa E, Gramatica P, Öberg T, Dao P, Cherkasov A, Tetko IV, "Combinatorial QSAR Modeling of Chemical Toxicants Tested Against Tetrahymena pyriformis." J Chem Inf Model. 2008; 48:766-784.

For further information about licensing the ADMET Predictor Toxicity module, please contact:
Mr. John DiBella
Director, Marketing & Sales
661-723-7723 ext. 244
john.dibella@simulations-plus.com