Advances in the approach to novel drug clinical development for breast cancer
Bibliographic record
Abstract
INTRODUCTION: In the post-genomic era clinical development of new agents to treat breast cancer (BC) can be a real challenge. Different from chemotherapy agents, with a broad but not specific spectrum of activity, novel drugs are being developed as 'targeted' agents, potentially benefiting a subgroup of patients. In BC, different clinically identifiable subtypes are now separately addressed in specific clinical trials. AREAS COVERED: In this review, the authors discuss the clinical development of targeted drugs that have become part of the current treatment of BC. They also highlight the challenges that in other cases determined the failure of promising compounds. Furthermore, the article reports on how combinations of targeted agents have emerged as valid strategies to overcome acquired resistance. It also provides discussion of how 'old' therapies can be retargeted to certain patient populations or 'reinvented' as safer and more effective with the creation of drug conjugates. They also discuss how novel clinical trial designs are emerging to accelerate the successful matching of targeted drugs to the right patient population. EXPERT OPINION: It is important not to forget that the development of BC therapeutics is a 'moving target', as its biology evolves in time under the pressure of ongoing treatments. There are currently a finite number of resources available for the development of new therapeutics, which means that resources need to be carefully allocated. There is also a need to prioritize clinical trials that can reduce the number of patients who are candidates for expensive treatments.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".