Application of Physiologically Based Pharmacokinetic Modeling to Inform Dose Selection of Mezigdomide in a Phase I Drug–Drug Interaction Study
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Bibliographic record
Abstract
Mezigdomide (MEZI) is an oral, highly potent CELMoD™ agent with promising antitumor and immune-stimulatory activity, optimized for Aiolos and Ikaros degradation. Preclinical evidence suggests MEZI is primarily metabolized by cytochrome P450 (CYP) 3A4/5 and has the potential to inhibit efflux transporters P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) in vitro. To predict the magnitude of enzyme- and transporter-mediated drug-drug interactions (DDI) and inform clinical study design, a physiologically based pharmacokinetic (PBPK) model was developed. A PBPK-informed Phase I clinical DDI study was conducted that evaluated MEZI as an object of CYP3A induction (rifampin) and inhibition (itraconazole) and as a precipitant of transporter-mediated interactions (digoxin and rosuvastatin). PBPK modeling predicted substantial interactions with strong and moderate CYP3A modulators, which informed a unique dose selection strategy, PK sampling time, and washout period. Clinical results confirmed reductions in MEZI exposure with rifampin (AUC reduced 93-95%) and increases with itraconazole (~14-fold for dose normalized AUC). MEZI was well-tolerated despite these changes in exposure. Additionally, coadministration of MEZI with P-gp and BCRP substrates, digoxin and rosuvastatin, showed no clinically meaningful changes in substrate plasma PK, indicating a low likelihood of significant transporter-mediated DDIs. The prospective PBPK model was refined with clinical data, improving predictions and supporting simulations for moderate/weak CYP3A modulators. This iterative "learn-confirm" approach underscores the utility of PBPK modeling in optimizing clinical trial design, ensuring participant safety, and anticipating DDI risks. The findings support MEZI's clinical development with informed dosing strategies, particularly for coadministration with CYP3A modulators.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it