Temporal trends in oncology drug revenue among the world’s major pharmaceutical companies: A 2010-2019 cohort study.
Notice bibliographique
Résumé
6505 Background: In the past decade there has been a 70% increase in the number of clinical trials for cancer drugs. During this time, there has also been a substantial increase in the price of cancer drugs. It is unclear how these trends have changed the revenue landscape of major pharmaceutical companies. In this study we characterize temporal trends in cancer drug revenue relative to non-cancer drugs. Methods: This retrospective cohort study used publicly available global sales data from the 10 pharmaceutical companies with the highest annual revenue in 2019; Abbvie (AB), AstraZeneca (AZ), Bristol Myers Squibb (BMS), GlaxoSmithKline (GSK), Johnson & Johnson (JJ), Merck (M), Novartis (N), Pfizer (P), Roche (R) and Sanofi (S). We quantified the contribution of cancer drugs to net revenue for each company from 2010 – 2019 using consolidated annual financial reports (i.e. 10-K or 20-F forms). Cancer drugs were defined as those with an FDA-approved indication for anti-cancer effect or supportive care. All sales data were converted to USD and adjusted for global inflation. Trends in the percentage of company revenues accounted for by cancer drugs were assessed with the Kendall-Mann test. P-values were adjusted for multiple hypothesis testing using the Benjamini-Hochberg method. Results: During 2010-2019, cumulative annual revenue generated from cancer drugs in our cohort of companies (n = 10) increased by 96%, from $52.8 billion to $103.5 billion. The cumulative revenue from non-oncology drugs decreased by 19%, from $342.5 billion to $276.9 billion. The proportion of total revenue generated from cancer drugs grew over time; from 13% in 2010 to 27% in 2019 (p < 0.001). During 2015-2019, annual revenue for the study cohort grew by 12%: from $339.7 billion to $380.4 billion. During this period non-oncology revenues remained stagnant (mean $278.9 billion, range 276.9 – 281.9), while oncology revenues grew by 66%; from $61.4 billion to $103.5 billion. Six companies (AB, AZ, BMS, JJ, N, and P) saw substantial increases in the proportion of revenue attributable to cancer drugs. R had both the highest net revenue ($23.9 billion), and highest proportion of revenue (57%) from cancer drugs in 2010 among the cohort, similar to 2019 ($27.7 billion, 57%; p = 0.37). While not reaching significance over the total study period, M saw increases in oncology revenue from $1.5 billion in 2015 to $12.3 billion in 2019 (4% to 30% of total revenue); driven almost exclusively by sales of Pembrolizumab. Conclusions: Amongst the world’s largest pharmaceutical companies, sales revenue from cancer drugs have increased by 96% over the past decade, while revenues from non-cancer drugs have decreased by 19%. Revenues from cancer drugs accounted for 27% of company revenues in 2019. Further work is needed to understand if this massive increase in sales revenues has translated into proportional improvements in patient and population outcomes.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».