Preclinical Pharmacokinetics of MI-219, a Novel Human Double Minute 2 (HDM2) Inhibitor and Prediction of Human Pharmacokinetics
Bibliographic record
Abstract
PURPOSE: The two purposes of this study were evaluating preclinical pharmacokinetics of MI-219 and predicting clearance (CL) and volume of distribution at steady-state (Vdss) of MI-219 in humans. METHODS: Pharmacokinetic studies were conducted on mice, rats, dogs, and monkeys. Human CL of MI-219 was predicted using allometric scaling (SA), multi-exponential allometric scaling (ME), rule of exponents (RoE), single species scaling, two-term power equation (TTPE), physiologically based in vitro-in vivo extrapolation (IVIVE), and fu corrected intercept method (FCIM). In vitro assays were conducted to determine in vitro intrinsic CL, protein binding, and blood-plasma partition coefficients. To estimate half-life of MI-219, plasma concentration-time profile in humans was predicted using kallynochron and apolysichron time transformation (Dedrick plots) and normalization with MRT and Vdss (Wajima's method). In addition, simultaneous interspecies scaling of CL, Vdss and concentration-time profile were performed by using Nonlinear Mixed Effects Modeling (NONMEM). RESULTS: Preclinical studies showed that the elimination of MI-219 was mainly through metabolism. The validation using observed monkey CL and Vdss showed that MA, IVIVE and Oie-Tozer methods were accurately than the other methods. Human CL of MI-219 predicted by ME and IVIVE was between 0.237-0.342 L*h⁻¹*kg⁻¹. Human Vdss predicted by Oie-Tozer method and allometric scaling of unbound volume of distribution of tissues (VT/fuT) method was between 0.93-1.40 L*kg⁻¹. Superimposition of rat, monkey and dog data was observed in Dedrick plots and Wajima's transformations. CONCLUSIONS: The predicted human pharmacokinetics is useful for the design of first-in-human study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".