The Liver Partition Coefficient-Corrected Inhibitory Quotient and the Pharmacokinetic-Pharmacodynamic Relationship of Directly Acting Anti-Hepatitis C Virus Agents in Humans
Bibliographic record
Abstract
Pharmacokinetic-pharmacodynamic (PK-PD) data analyses from early hepatitis C virus (HCV) clinical trials failed to show a good correlation between the plasma inhibitory quotient (IQ) and antiviral activity of different classes of directly acting antiviral agents (DAAs). The present study explored whether use of the liver partition coefficient-corrected IQ (LCIQ) could improve the PK-PD relationship. Animal liver partition coefficients (Kp(liver)) were calculated from liver to plasma exposure ratios. In vitro hepatocyte partition coefficients (Kp(hep)) were determined by the ratio of cellular to medium drug concentrations. Human Kp(liver) was predicted using an in vitro-in vivo proportionality method: the species-averaged animal Kp(liver) multiplied by the ratio of human Kp(hep) over those in animals. LCIQ was calculated using the IQ multiplied by the predicted human Kp(liver). Our results demonstrated that the in vitro-in vivo proportionality approach provided the best human Kp(liver) prediction, with prediction errors of <45% for all 5 benchmark drugs evaluated (doxorubicin, verapamil, digoxin, quinidine, and imipramine). Plasma IQ values correlated poorly (r(2) of 0.48) with maximum viral load reduction and led to a corresponding 50% effective dose (ED(50)) IQ of 42, with a 95% confidence interval (CI) of 0.1 to 148534. In contrast, the LCIQ-maximum VLR relationship fit into a typical sigmoidal curve with an r(2) value of 0.95 and an ED(50) LCIQ of 121, with a 95% CI of 83 to 177. The present study provides a novel human Kp(liver) prediction model, and the LCIQ correlated well with the viral load reductions observed in short-term HCV monotherapy of different DAAs and provides a valuable tool to guide HCV drug discovery.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".