Novel drugs approved by the EMA, the FDA and the MHRA in 2024: a year in review
Bibliographic record
Abstract
In the past year, the FDA, the EMA or the MHRA authorised fifty-three (53) novel drugs. While this 2024 harvest is not as rich as that of 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 very dynamic development of therapeutics in areas of high unmet need. 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 such 2024 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 authorized direct telomerase inhibitor (imetelstat) that reprogrammes the tumour cells’ oncogenic drive. In addition, in 2024 an impressive percentage of the novel drugs were first-in-class (28 out of 53, or 53% of the total) and a substantial number of them can be considered disease-agnostic, indicating the possibility of future approved extension of their use into additional indications. Overall, 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".