Application of Physiologically Based Pharmacokinetic Modeling to Inform Dose Selection of Mezigdomide in a Phase I Drug–Drug Interaction Study
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".