In Silico-based Study of Cytochrome P450 and Multidrug Resistance Protein 1 from Docking Perspective to Understand Kidney Failure
Bibliographic record
Abstract
Background: The need to identify causes of drug induced kidney failure has been underscored by International Conference on Harmonization (ICH) regulated agencies. In our earlier studies on adverse drug reaction (ADR) reported in Canada Vigilance Adverse Reaction Online Database it was observed that drugs azathioprine, clozaril/clozapine, diclofenac sodium, diflucan/ fluconazole, furosemide, indomethacin, metformin, micardis/ telmisartan, viread/tenofovir, and zyprexa/olanzapine lead to kidney failure. Method: Attempts have been made to understand the physiological process via bioinformatics perspective. This was done by active site identification for cytochrome P450 along with multidrug resistance protein 1 (MRP1). Docking against the drugs in these proteins that are categorically involved in drug binding based on their pharmacological actions are as per drug bank annotations. Results: Cytochrome P450 2C19 protein showed better interactions with drug indomethacin with a maximum score of –119.2 kcal/mol followed by drug clozaril with a score of –102.5 kcal/mol. This was finally followed by of drug zyprexa with a score of –101.0 kcal/mol. The residues which are actively involved with the drug indomethacin include Arg97 and Arg433. Drug clozaril shows interaction with Ala297. For drug zyprexa the residues like Arg97, Ala297 and Cys435 interact with the protein. For MRP1, even though it showed better binding scores for drugs azathioprine, indomethacin, diflucan and furosemide. But still, they are not able to interact within the pocket, leaving it empty during docking studies. Conclusion: Through this study, it was possible to identify active site pocket in the related proteins and the interacting amino acid residues of cytochrome P450 that may contribute to drug induced kidney failure.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".