A Comprehensive Spectroscopic and Computational Investigation to Probe the Interaction of Antineoplastic Drug Nordihydroguaiaretic Acid with Serum Albumins
Bibliographic record
Abstract
Exogenous drugs that are used as antidote against chemotheray, inflammation or viral infection, gets absorbed and interacts reversibly to the major serum transport protein i.e. albumins, upon entering the circulatory system. To have a structural guideline in the rational drug designing and in the synthesis of drugs with greater efficacy, the binding mechanism of an antineoplastic and anti-inflammatory drug Nordihydroguaiaretic acid (NDGA) with human and bovine serum albumins (HSA & BSA) were examined by spectroscopic and computational methods. NDGA binds to site II of HSA with binding constant (Kb) ~105 M-1 and free energy (ΔG) ~ -7.5 kcal.mol-1. It also binds at site II of BSA but with lesser binding affinity (Kb) ~105 M-1 and ΔG ~ -6.5 kcal.mol-1. The negative value of ΔG, ΔH and ΔS for both the albumins at three different temperatures confirmed that the complex formation process between albumins and NDGA is spontaneous and exothermic. Furthermore, hydrogen bonds and hydrophobic interactions are the main forces involved in complex formation of NDGA with both the albumins as evaluated from fluorescence and molecular docking results. Binding of NDGA to both the albumins alter the conformation and causes minor change in the secondary structure of proteins as indicated by the CD spectra.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".