Opiod Structure Activity Relationship. 1. Structure Activity Relationship of Opioids Dr. Suji V. S. Dept of Pharmacology. 2. Classification of. which explains structure-activity relationships of opiate drugs, including (l) the Examples in- model. Opiate agonists, such as methadone and propoxyphene. methadone morphinans Structure Activity Relationship detailed d. partial agonists. Agent Class. Example. Action. Agonist morphine fentanyl pethidine.
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.
This data set, compiled using the data mining procedure, suffers some limitations. Firstly it does not make any distinction between idiosyncratic and dose-dependent toxicity. Idiosyncratic toxicity refers to an abnormal reaction to a drug that is not connected to its pharmacological activity but is due to individual hypersensitivity Cheng and Dixon, ; Russmann et al.
This toxicity does not follow any specific mode of action, but the adverse reactions to drugs are of unknown etiology and involved only a small proportion of the population Walgren et al. This means that where information is lacking it has been assumed that the compound was negative.
Even if it is true that for well-known and investigated drugs, the lack of information can be taken as negative Hewitt et al. When the hepatocyte membrane is damaged these enzymes, which are normally located in the cytosol, are released into the bloodstream Pari and Murugan, Although the serum transaminases are commonly used as indicators of liver injury and reflect damage to hepatocytes Ozer et al. For example, ALT and AST are present in other tissues heart, brain and skeletal muscle besides the liver and so they are released into the circulation when there is damage to these tissues.
AST mostly increases in case of myocyte damage due to extreme physical effort Ozer et al. LDH is another enzyme occasionally used as a biomarker of hepatocellular injury. However, it is not routinely employed since its specificity is questionable Ramaiah, More recently genomics, proteomics and metabolomics have been proposed as valuable techniques for discovering biomarkers Amacher et al.
However, the most of the datasets is not suitable to be used alone for classification modeling. In conclusion, the data we used for modeling have a certain level of uncertainty due to these points which may have influenced the reliability and performance of the model. An alternative that could limit the uncertainty linked to hepatotoxicity data is to use in vitro data obtained if possible on the same cell lines and using the same laboratory assay and conditions.
However, not much public data is available in the open literature for this purpose and this approach too suffers some limitations such as the influence of genetic and environmental factors in the variations of biochemistry Przybylak and Cronin, Mechanistic Explanation of SAs We propose, when possible, a mechanistic rationale using the information in the literature and in public databases PubChem https: N-Containing Heterocyclic Aromatic Compounds: 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.
Methadone | C21H27NO - PubChem
For example, conflicting activity data from homogenate receptor studies will not supercede data from in vivo human studies and conflicting structural determinations from computational chemistry will not supercede results from stereochemistry, crystallography or NMR studies.
Thus, observations from the literature can be weighted from most significant to least in the following manner: Exceptions to the propositions may exist because numerous opioid and opioid peptides have been synthesized prior to recognition of multiple opioid receptors. Also refinements in laboratory techniques may have changed data interpretation.
However, this argument applies to active opioids and opioid peptides which produce effects in humans. This theory relies heavily on the stereochemistry of the opioids to explain pharmacologic activity of opioids and opioid peptides.
Although computational measurements from other authors are considered, the focus has been to describe the pharmacologic activities through comparisons of enantiomers which become evident in the presence of virtual or known heterocyclic rings. Steric effects hindering the heterocyclic ring by various isomers explain agonist and antagonist characteristics of the molecule. Further work in the form of computational chemistry and experimental pharmacology may support or refute this theory.
Previous Structure Activity Theories Beckett and Casy proposed that an aromatic and a basic amine, which is protonated at physiologic pH, exists to form a 3 point model consisting of an anionic site Nhydrophobic region of a piperidine ring, and a phenolic site tyrosine. Kane et al described an opioid agonist model suggesting that multiple epitopes exist for ligand binding. They did not extend their theory to opioid peptides.
Cometta-Morini et al proposed a structure activity relationship for the fentanyl classes of compounds which relied upon four key moieties.
Rationale for Selection of Opioids to Investigate The opioids considered for this paper represent a subset of those compounds known to produce analgesia through the mu opioid receptor in man excluding many of the analgesics where bioavailability, lipid solubility, or metabolism may predict differences in action.
Just as clinical data is prioritized in reviews or meta-analysis, the selected compounds of highest priority clinical response in man were investigate which by- passed the in vitro—in vivo correlation discrepancies.