Impact of Transporter Polymorphisms on Drug Development: Is It Clinically Significant?
Bibliographic record
Abstract
Drug transporters are becoming increasingly recognized as relevant to the drug development process. This may be a reflection of increasing target complexity and the need for high-affinity interaction with drug targets that minimize off-target side effects. Moreover, as new molecular entities (NMEs) become larger in size and amphipathic in nature, interaction with drug transporters, both uptake as well as efflux, becomes increasingly likely. In some cases transporters may limit the absorption or organ-specific entry of NMEs, whereas in other cases transporters may enhance their absorption or tissue accumulation. Indeed, in some cases, transporters may prove to be a therapeutic target. Accordingly, a better understanding of potentially clinically relevant drug transporter polymorphisms earlier in the drug development process is highly desirable. In this review we examine key transporters that are important to the absorption, distribution, and excretion of a large number of drugs in clinical use. Importantly, we provide our assessment of the potential impact of known polymorphisms in such transporters and discuss whether there is sufficient evidence to incorporate these polymorphisms in the drug development process.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".