Natural product-derived compounds in clinical trials and drug approvals
Bibliographic record
Abstract
, 1612Natural products (NPs) have long been foundational in medicine, from ancient herbal remedies to the discovery of transformative drugs like morphine and quinine. The mid-20th century marked a 'golden age' for antibiotic discovery from natural sources, which then expanded into other therapeutic areas. However, by the late 20th century, other technological advances had shifted NPs from being a central component of the discovery process to one of several options. This review explores the current role of NPs in pharmaceuticals by analysing NP-derived (NP-D) drugs approved since 2014 and clinical candidates in development as of the end of 2024. 58 NP-related drugs launched between January 2014 and June 2025 were identified, which included 45 NP and NP-D new chemical entities (NCEs) and 13 NP-antibody drug conjugates (NP-ADCs). Next, all 579 drugs-388 (67%) of which were NCEs and 191 (33%) were new biological entities (NBEs)-approved globally from 2014 to 2024 were analysed. In total, 56 (9.7%) of these 579 drugs were classified as NPs or NP-Ds using this review's NP definition: 44 NCEs (7.6% overall; 11.3% of NCEs) and 12 NP-ADCs (2.1% overall; 6.3% of NBEs). The number of new NP-D NCEs and NP-ADCs has fluctuated between 0 and 8 annually since 2014, with an average of five approvals per year. Next, 125 NP and NP-D compounds were identified that were undergoing clinical trials or in the registration phase at the end of December 2024. Thirty-three new pharmacophores not previously found in approved drugs are now in development; however, only one has been discovered in the past 15 years. This review highlights the enduring promise of NPs, despite their diminished role in drug discovery, and advocates for renewed emphasis on bioassay-guided isolation and mode of action studies to identify new drug leads.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".