To Be Drug or Prodrug: Structure-Property Exploratory Approach Regarding Oral Bioavailability
Bibliographic record
Abstract
PURPOSE: Prodrug design is a strategy that can be used to adjust physicochemical properties of drugs in order to overcome pharmacokinetic problems, such as poor oral bioavailability. However, Lipinski´s and Veber´s rules predict whether compounds will have absorption problems even before the design of prodrugs. In this context, our goal was to evaluate the molecular properties which most influenced the absorption process of prodrugs compared to its precursor through exploratory data analysis approach. METHODS: A variety of prodrugs and respective precursors were randomly selected and classified by its percentage of human intestinal absorption. Subsequently, different molecular properties were calculated and hierarchical cluster analysis (HCA) and principal components analysis (PCA) were carried out. RESULTS: According to the findings, antiviral, anti-hypertensive, and antibiotic prodrugs exhibited higher absorption levels than their respective precursors. Also, some relevant descriptors (molecular weight, MW, routable bonds, rot_bonds, hydrogen bond acceptors, HBA_count and polar surface area, PSA), which are included in Lipinski´s and Veber´s rules, influenced the separation process between prodrugs and drugs. Furthermore, other molecular properties, such as polarizability (α) and molar refractivity (MR), were pointed out. CONCLUSION: Lipinski´s and Veber´s rules proved to be important to design an orally administered drug but other descriptors should be considered by medicinal chemists in the prodrug designing process.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".