Transporters in Drug Development: International Transporter Consortium Update on Emerging Transporters of Clinical Importance
Bibliographic record
Abstract
During its fourth transporter workshop in 2021, the International Transporter Consortium (ITC) provided updates on emerging clinically relevant transporters for drug development. Previously highlighted and new transporters were considered based on up-to-date clinical evidence of their importance in drug-drug interactions and potential for altered drug efficacy and safety, including drug-nutrient interactions leading to nutrient deficiencies. For the first time, folate transport pathways (PCFT, RFC, and FRα) were examined in-depth as a potential mechanism of drug-induced folate deficiency and related toxicities (e.g., neural tube defects and megaloblastic anemia). However, routine toxicology studies conducted in support of drug development appear sufficient to flag such folate deficiency toxicities, whereas prospective prediction from in vitro folate metabolism and transport inhibition is not well enough established to inform drug development. Previous suggestion of a retrospective study of intestinal OATP2B1 inhibition to explain unexpected decreases in drug exposure were updated. Furthermore, when the absorption of a new molecular entity is more rapid and extensive than can be explained by passive permeability, evaluation of the OATP2B1 transport may be considered. Emerging research on hepatic and renal OAT2 is summarized, but current understanding of the importance of OAT2 was deemed insufficient to justify specific consideration for drug development. Hepatic, renal, and intestinal MRPs (MRP2, MRP3, and MRP4) were revisited. MRPs may be considered when they are suspected to be the major determinant of drug disposition (e.g., direct glucuronide conjugates); MRP2 inhibition as a mechanistic explanation for drug-induced hyperbilirubinemia remains justified. There were no major changes in recommendations from previous ITC whitepapers.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".