Indian vaccine innovation: the case of Shantha Biotechnics
Notice bibliographique
Résumé
BACKGROUND: Although the World Health Organization had recommended that every child be vaccinated for Hepatitis B by the early 1980s, large multinational pharmaceutical companies held monopolies on the recombinant Hepatitis B vaccine. At a price as high as USD$23 a dose, most Indians families could not afford vaccination. Shantha Biotechnics, a pioneering Indian biotechnology company founded in 1993, saw an unmet need domestically, and developed novel processes for manufacturing Hepatitis B vaccine to reduce prices to less than $1/dose. Further expansion enabled low-cost mass vaccination globally through organizations such as UNICEF. In 2009, Shantha sold over 120 million doses of vaccines. The company was recently acquired by Sanofi-Aventis at a valuation of USD$784 million. METHODS: The case study and grounded research method was used to illustrate how the globalization of healthcare R&D is enabling private sector companies such as Shantha to address access to essential medicines. Sources including interviews, literature analysis, and on-site observations were combined to conduct a robust examination of Shantha's evolution as a major provider of vaccines for global health indications. RESULTS: Shantha's ability to become a significant global vaccine manufacturer and achieve international valuation and market success appears to have been made possible by focusing first on the local health needs of India. How Shantha achieved this balance can be understood in terms of a framework of four guiding principles. First, Shantha identified a therapeutic area (Hepatitis B) in which cost efficiencies could be achieved for reaching the poor. Second, Shantha persistently sought investments and partnerships from non-traditional and international sources including the Foreign Ministry of Oman and Pfizer. Third, Shantha focused on innovation and quality - investing in innovation from the outset yielded the crucial process innovation that allowed Shantha to make an affordable vaccine. Fourth, Shantha constructed its own cGMP facility, which established credibility for vaccine prequalification by the World Health Organization and generated interest from large pharmaceutical companies in its contract research services. These two sources of revenue allowed Shantha to continue to invest in health innovation relevant to the developing world. CONCLUSIONS: The Shantha case study underscores the important role the private sector can play in global health and access to medicines. Home-grown companies in the developing world are becoming a source of low-cost, locally relevant healthcare R&D for therapeutics such as vaccines. Such companies may be compelled by market forces to focus on products relevant to diseases endemic in their country. Sanofi-Aventis' acquisition of Shantha reveals that even large pharmaceutical companies based in the developed world have recognized the importance of meeting the health needs of the developing world. Collectively, these processes suggest an ability to tap into private sector investments for global health innovation, and illustrate the globalization of healthcare R&D to the developing world.
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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 | 0,007 | 0,006 |
| 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 | 0,015 | 0,019 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».