Novel drugs approved by the EMA, the FDA and the MHRA in 2024: A year in review
Bibliographic record
Abstract
Abstract In the past year, the European Medicines Agency (EMA), the Food and Drug Administration (FDA) and the Medicines and Healthcare Products Regulatory Agency (MHRA) authorised 53 novel drugs. While the 2024 harvest is not as rich as in 2023, when 70 new chemical entities were approved, the number of ‘orphan’ drug authorisations in 2024 (21) is similar to that of 2023 (24), illustrating the dynamic development of therapeutics in areas of unmet need. The 2024 approvals of novel protein therapeutics (15) and advanced therapy medicinal products (ATMPs, 6) indicate a sustained trend also noticeable in the 2023 new drugs reviewed in this journal last year (16 and 11, respectively). Clearly, the most striking characteristic of the 2024 drug yield is the creative pharmacological design, which allows these medicines to employ a novel approach to target a disease. Some notable examples are the first drug successfully using a ‘dock‐and‐block’ mechanism of inhibition (zenocutuzumab), the first approved drug for schizophrenia designed as an agonist of M1/M4 muscarinic receptors (xanomeline), the first biparatopic antibody (zanidatamab), binding two distinct epitopes of the same molecule, the first haemophilia therapy that instead of relying on external supplementation of clotting factors, restores Factor Xa activity by inhibiting TFPI (marstacimab), or the first ever authorised direct telomerase inhibitor (imetelstat) that reprogrammes the oncogenic drive of tumour cells. In addition, an impressive percentage of novel drugs were first in class (28 out of 53 or 53% of the total) and a substantial number can be considered disease agnostic, indicating the possibility of future approved extensions of their use for additional indications. The 2024 harvest demonstrates the therapeutic potential of innovative pharmacological design, which allows the effective targeting of intractable disorders and addresses crucial, unmet therapeutic needs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".