The road to commercialization in Africa: lessons from developing the sickle-cell drug Niprisan
Bibliographic record
Abstract
BACKGROUND: Developing novel drugs from traditional medicinal knowledge can serve as a means to improve public health. Yet countries in sub-Saharan Africa face barriers in translating traditional medicinal knowledge into commercially viable health products. Barriers in moving along the road towards making a new drug available include insufficient manufacturing capacity; knowledge sharing between scientists and medical healers; regulatory hurdles; quality control issues; pricing and distribution; and lack of financing. The case study method was used to illustrate efforts to overcome these barriers during the development in Nigeria of Niprisan - a novel drug for the treatment of sickle cell anemia, a chronic blood disorder with few effective therapies. DISCUSSION: Building on the knowledge of a traditional medicine practitioner, Nigeria's National Institute for Pharmaceutical Research and Development (NIPRD) developed the traditional herbal medicine Niprisan. The commercialization of Niprisan reached a number of commercial milestones, including regulatory approval in Nigeria; securing US-based commercial partner XeChem; demonstrating clinical efficacy and safety; being awarded orphan drug status by the US Food and Drug Administration; and striking important relationships with domestic and international groups. Despite these successes, however, XeChem did not achieve mainstream success for Niprisan in Nigeria or in the United States. A number of reasons, including inconsistent funding and manufacturing and management challenges, have been put forth to explain Niprisan's commercial demise. As of this writing, NIPRD is considering options for another commercial partner to take the drug forward. SUMMARY: Evidence from the Niprisan experience suggests that establishing benefit-sharing agreements, fostering partnerships with established research institutions, improving standardization and quality control, ensuring financial and managerial due diligence, and recruiting entrepreneurial leaders capable of holding dual scientific and business responsibilities should be incorporated into future drug development initiatives based on traditional medicines. Country-level supporting policies and conditions are also important. With more experience and support, and an improved environment for innovation, developing new drugs from traditional medicines may be an attractive approach to addressing diseases in sub-Saharan Africa and other regions.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".