PHARMACEUTICALS
individually , indicating sustainability factors that need to be addressed but also questioning the potential for some regulatory impacts for example highlighting the presence of ICH Level 1 and / or 2 solvents which should be removed or changed in order to make the overall process more sustainable .
Whilst PRIME is not focused only on solvents , we know from our data that in a chemical reaction they make up more than 90 % of a reaction volume . We guide our development in the following order : avoid , reduce , re-use , recover or transform .
The avoidance of solvents is a challenge for the entire industry . Hovione is exploring technologies like micellar chemistry or mechanochemistry , which can completely remove or substantially
Hovione research centre laboratories
reduce solvent volumes . When it is not possible to avoid solvent use , we use our internal database to search for a greener solvent . This considers five different factors to provide the scientist with the most sustainable solvent with less environmental impact :
• Health risk
• Process safety
• Environmental impact
• Sustainability
• Globally Harmonised System codes for carcinogenic , mutagenic and reprotoxic substances This tool is used alongside PRIME to accelerate sustainable process development . Regardless of the solvent used , chemists strive to minimise the total volumes required for reactions while ensuring compatibility with production-scale equipment . The implementation of solvent recycling or recovery poses a significant challenge in drug substance processes . However , it also offers substantial benefits , particularly for larger commercial projects .
Conclusion & future vision
The integration of data-driven approaches , AI technologies and sustainable practices holds tremendous promise for advancing pharmaceutical development . By leveraging standardised data formats , advanced tools and AI-driven insights , researchers can accelerate drug discovery and development processes , optimise manufacturing operations and mitigate environmental impact , paving the way for a more sustainable and innovative pharmaceutical industry .
With tools like PRIME and others , we can predict and achieve targeted process scores . As we explore alternative methods to develop and manufacture medicines with reduced solvent usage , we increasingly rely on computational power to reduce the number of experiments performed in the lab .
This is complemented by the generation of models and digital twins that can predict the process and equipment interactions enhancing initial accuracy rates when moving up in scale . The use of AI is essential to achieve a more sustainable future and leveraging historical knowledge is essential to make it happen . ●
* - The author would like to thank the collaboration of Ricardo Mendonça , Carmel Pyne , Susana Lucas , Jon Peers , Jaime del Campo , Filipe Ataide and Will Frost for all their support and revision during the writing of this paper
Rui Loureiro
R & D FELLOW – SUSTAINABILITY LEAD
References : 1 : https :// www . nature . com / articles / d41573-021-001909 . epdf ? no _ publisher _ access = 1 & r3 _ referer = nature 2 : J . K . Smith & L . M . Johnson , Drug Discovery Today , 2020 , 25 ( 7 ), 1123-1135 : https :// doi . org / 10.1016 / j . drudis . 2020.03.007 3 : M . Kopach & E . Reiff , Future Med . Chem ., 2020 , 4 ( 11 ) 1395-1398 : doi10.4155 / fmc . 12.78 4 : P . T . Anastas & J . C . Warner , Green Chemistry : Theory and Practice . Oxford University Press , 1988 5 : M . C . Bryan et al ., Green Chemistry , 2018 , 20 ( 22 ), 5082-5103
HOVIONE k + 351 910342540 J rloureiro @ hovione . com j www . hovione . com
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