Application of Quantitative Structure-activity Relationships to Investigate Xenobiotic Cytotoxicity Mechanisms in Hepatocyte Systems
Bibliographic record
Abstract
Hepatotoxicity is a serious adverse health effect caused by drugs and other chemical toxins generally detected in the later stages of drug development or in whole animal studies. Thus, development of screening approaches available for earlier identification of hepatotoxic molecules is necessary. A novel in vitro- in silico test system for the evaluation of the molecular mechanisms of xenobiotic toxicity in primary hepatocyte systems is presented here. It is well established that hepatocytes in vitro are most representative of hepatotoxicity in vivo, and are most useful for the determination of xenobiotic hepatotoxicity mechanisms at the molecular and cellular level. There is an on-going interest in Quantitative Structure-Activity Relationships (QSAR) in toxicology, as it can identify correlations between chemical structure and biological activity. QSAR can be used to evaluate the effects of metabolism and toxicity as many physicochemical descriptors reflect simple molecular properties that can provide insight into the physicochemical nature of the activity under consideration. QSARs were determined for hepatotoxicity of halobenzenes, p-benzoquinones, α,β-unsaturated carbonyl compounds and nitroaromatics towards isolated hepatocytes. A molecular link was established for their proposed toxicity pathways. For example oxidative activation was linked to EHOMO (energy of the highest occupied molecular orbital) values and hydrophobicity (log P) of the chemicals, while reductive activation was linked with ELUMO (energy of the lowest molecular orbital) values and log P. Such relationships may thus be useful for predicting toxicity of other chemicals of the same mechanism of toxicity. Due to the complexity involved in the phenomena of hepatotoxicity, unravelling of structure-hepatotoxicity relationships is a complicated task. A conceptual framework for QSAR modeling is proposed that involves recognition of molecular initiating events as potential endpoints to improve the prediction potential of QSAR models. Acute toxicity of reactive chemicals could be based on an initial reaction with biomolecules, thus the theory of covalent binding reactivity was used to test this concept. Reactivity assays with thiol and amine surrogate nucleophiles were used to determine susceptibility to toxicity. The derived QSAR expressions suggested that covalent binding reactivity is a good correlate to hepatotoxicity, however only if electrophilicity was the main mechanism of toxicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".