Combining retrosynthesis and mixed-integer optimization for minimizing the chemical inventory needed to realize a WHO essential medicines list
The access to essential medicines remains a problem in many low-income countries for logistic and expiration limits, among other factors. Enabling flexible replenishment and easier supply chain management by on demand manufacturing from stored starting materials provides a solution to this challenge...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Published: |
Royal Society of Chemistry (RSC)
2020
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| Online Access: | https://hdl.handle.net/1721.1/123797 |
| Summary: | The access to essential medicines remains a problem in many low-income countries for logistic and expiration limits, among other factors. Enabling flexible replenishment and easier supply chain management by on demand manufacturing from stored starting materials provides a solution to this challenge. Recent developments in computer-aided chemical synthesis planning have benefited from machine learning in different aspects. In this manuscript, we use those techniques to perform a combined analysis of a WHO essential medicines list to identify synthetic routes that minimize chemical inventory that would be required to synthesize the all the active pharmaceutical ingredients. We use a synthesis planning tool to perform retrosynthetic analyses for 99 targets and solve a mixed-integer programming problem to select a combination of pathways that uses the minimal number of chemicals. This work demonstrates the technical feasibility of reducing storage of active pharmaceutical ingredients to a minimal inventory of starting materials. |
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