Structure–activity relationship (SAR) models can be used to predict the biological activity of potential developmental toxicants whose adverse effects include. This chapter reviews the evolution of structure-activity relationship studies and suggests estrogen receptor models to predict biological activity. A brief survey of . of structure-activity relationships in vivo using Allen Doisy or 3-day uterine weight tests can .. ethinylestradiol, and mestranol: (A) inhibition of the binding of.
DILI is therefore poorly understood and hard to predict. Early identification of DILI is essential in order primarily to increase drug safety but also to reduce the costs of drug development. Besides in vitro techniques which anyway are expensive and time-consuming, interest is rising in computational tools for predicting toxicity that can evaluate and screen large numbers of compounds in a limited time and affect the attrition rates of compounds in drug discovery and development phases Muster et al.
Commercial software exists for the prediction of human toxic endpoints such as mutagenicity, carcinogenicity, developmental and reproductive toxicity, skin and eye irritation.
However, the prediction of toxicity at organ level is still a challenge on account of the complex intrinsic nature of mechanisms of toxicity and the paucity of reliable in vivo and in vitro data Cheng and Dixon, Despite the objective hurdles to modeling DILI, some in silico tools for the prediction of hepatotoxicity have been developed through most of them are commercial.
The models for in silico assessment of hepatotoxicity have been recently reviewed by Przybylak and Cronin and Chen et al.
Among computational models, quantitative structure-activity relationship QSAR and structure-activity relationship SAR are the most used ones. QSAR models quantitatively examine the toxicological activity of a compound starting from its chemical structure, on the principle that similar chemical substances should have similar biological behavior. SAR focuses on the rule determining the relationship, as a classifier Pery et al. Considering the model structure, in silico models can be divided in two main groups: Besides software, other in silico models based on SAs have been recently described in the literature Egan et al.
This model was built by developing automatically and manually-extracted SAs, which are chemical sub-structures linked to a particular activity or toxicity. The use of human data for building the model means the information provided can be used without the need to extrapolate the results from different species, reducing the uncertainty linked to inter-species variability.
Furthermore, this in silico model can be used as alternative to animal testing for screening purposes and will be implemented in the VEGA platform http: Materials and methods Hepatotoxicity data collection The first step was to collect data for modeling. Few public datasets on DILI are available. We focused on the following data sources since they were easily detectable and downloadable from the web and they were reliable since already used by other authors Chen et al.
The first was Fourches et al. These were extracted through a data mining approach based on a combination of lexical and linguistic methods and ontological rules in order to link substances to a series of liver diseases, searching the open literature. This database contains data from in vitro and in vivo studies and follows a simple classification approach: More details can be found in Fourches et al.
We selected only data referring to humans data and eliminated the rest. This contains unique pharmaceuticals, of which non-proprietary data have adverse drug reaction data for one or more of the 47 liver effects Coding Symbols for Thesaurus of Adverse Reaction COSTAR term endpoints Matthews et al. For each compound there is an overall activity category A for active, I for inactive and M for marginally active referring to five hepatic endpoints: Since only two compounds were labeled as M we eliminated them in order to reduce the uncertainty of the data set.
We merged the two data sets comparing the chemical structures of the compounds by using the software described in Floris et al. Proposition 4 The piperidine ring of fentanyl can assume the morphine position under conditions of nitrogen inversion. Proposition 5 The first 3 amino acid sequences of beta endorphin l-try-gly-gly and the active opioid dipeptide, l-tyr-pro, as a result of a peptide turn and zwitterion bonding form a virtual piperazine-like ring which is similar in size, shape and location to the heterocyclic rings of morphine, meperidine, and methadone.
Estradiol - DrugBank
Potential flaws in this theory are discussed. This theory could be important for future analgesic drug design. Hundreds of compounds have been synthesized and tested for improvements of alkaloids derived from the opium poppy. The simplest synthetic compounds which have extensive clinical use are meperidine and methadone.
Researchers continue to search for improved analgesics with fewer side effects, increased potency, and less risk of tolerance. The conformational similarities between morphine, meperidine, fentanyl, methadone and the endorphins are still speculative.
Although the endorphins are potent analgesics they have limited clinical use because they are inactivated during ingestion and cannot cross the blood brain barrier. It is hypothesized that a virtual or known heterocyclic ring exists in all opioids which have activity in humans and this ring occupies relative to the aromatic ring of the drug, approximately the same plane in space as the piperidine ring of morphine. General Premises of the Argument In humans, a single mu opioid receptor exists as defined by that structure of the central nervous system which binds morphine and endorphin and facilitates analgesia.
The clinical, animal, experimental, and computational information pertaining to opioid and opioid peptides is vast and spans two centuries. Some of the data may be inaccurate because laboratory and computing technologies have been refined during this time period. In order to develop a theory applicable to human pharmacology, the author chose to prioritize data in the literature.
Totally, we identified 13 SAs, 11 of them considered hepatotoxic. SA identified with ID 1 N-containing heterocycles aromatic compounds pyridine, pyrazine, pyrimidine matched the highest number of compounds in the training 57test 19 and external validation 9 sets.
Its performance in the training and in the test sets was high It did not match any chemicals in the external validation set. In the test and external validation sets it did not match any compounds.
Although we only used positive compounds to extract SAs, we labeled the SAs identified by ID 12 and 13 as non-hepatotoxic since they matched more experimentally non-hepatotoxic compounds than hepatotoxic ones Figure 1step 5.
In order to keep only the reliable SAs, we deleted those with percentages of TP below the arbitrary threshold of The complete list of SAs for hepatotoxicity and non-hepatotoxicity is available in Supplementary Table 2; the statistical performance of each SA, in terms of total number of occurrences and the number and percentage of TP in the training, test and external validation sets are also provided. Due to the relative high number of SAs extracted with SARpy software compared to the number of molecules available for the test and external validation sets, the total occurrences of 34 and 37 out of 75 SAs were null in the test and external validation set, respectively.
Decision Tree After identifying the SAs, we established a reasonable strategy for manually building the model basing on the expert-based knowledge.
Figure 2 shows the decision tree we applied for building the model for the prediction of hepatotoxicity. If more than one SAs is found, the prediction depends on the number of SAs: Since it is preferable to overestimate hepatotoxicity rather than not to recognize unsafe compounds, the overall model's architecture followed a conservative approach.
Decision tree developed for the hepatotoxicity model. Percentages of correctly predicted, wrongly predicted and non-predicted unknown compounds in the training, test and external validation sets. Performance of the model in the training, test and external validation sets. Out of compounds that were present in the training set, were not predicted by the model unknown, non-predicted.
For 91 compounds in the test set molecules the model did not provide any prediction unknown, non-predicted48 compounds were correctly identified as hepatotoxic TP and 15 as non-hepatotoxic TN. The number of experimentally negative non-hepatotoxic compounds wrongly predicted as hepatotoxic FP was 30 and the number of positive compounds hepatotoxic wrongly predicted as negative FN was 6.
In the external validation set compounds59 chemicals were not predicted by the model unknown, non-predictedthe numbers of TP and TN was 35 and 5 respectively. Figure 3 shows percentages of correctly predicted and wrongly predicted compounds in the training, test and external validation sets.
Discussion Limitations and Weaknesses of Experimental Hepatotoxicity Data High-quality and reliable biological data are essential in order to build predictive models to provide relevant information about the toxicological behavior of a substance. Ideally the data for building a model should be obtained using a unique, well-standardized protocol, in the same laboratory by the same scientists. It is also important that these data refer to a clear and unambiguous endpoint Cronin and Schultz, However, this is difficult, especially for hepatotoxicity, since the data are spread out in the literature and databases, refer to several endpoints related to hepatotoxicity steatosis, colestasis, fibrosis etc.
Then, as previously mentioned, there is no a good single standard indicator of DILI with high sensitivity and specificity Przybylak and Cronin, Indeed, no well-defined biomarkers exist for the identification of hepatotoxicity in vitro or in vivo.
Consequently, the data in the literature refer to different effects and mechanisms of action underlying the endpoint of hepatotoxicity.