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Background information on model inputs, assumptions and limitations, literature reviews, Life Cycle Assessment results by midpoint category and sensitivity analysis. (DOCX 4880 kb)

At an ever increasing rate innovative chemistry and technology platforms are reshaping manufacturing environments to become factories of the future by being more productive, lean and flexible. The use of Life Cycle Assessment (LCA) in early process development phases has been challenged many a time to assess whether or not this willingness to strive for innovation is an environmentally sustainable one, often due to a lack of process data. This study takes the streamlining of LCA one step further and proposes an optimal complexity of modelled product systems in terms of their optimal set of predictor variables. Out of more than 2,800 Basic Operations (BOs) in pharmaceutical synthesis steps, candidate predictor variables were identified to forecast the environmental burden (in this case natural resource consumption) of a production step per unit of output. By means of backwards stepwise linear regression modelling, combinations of candidate predictors were tested and evaluated based on their predictive power (R²) and the model uncertainty. It was proven that at least the amount of organic solvents used, the molar efficiency and the time duration of the synthesis step should be included in the model (R² = 0.87) as being the most significant predictor variables. Including additional predictors however imposes no guarantee to contribute to the predictive power and eventually weakens the model interpretation and its simplicity. The results of the study were evaluated in the light of the product-specific versus product group approach debate. Should LCAs be generalized to such an extent that an extensively diversified product group is to be represented with an averaged burden, while fairly simplified and streamlined methods can represent product-specific impact assessments with a reasonable need for data, time and knowledge? The trade-off between simplicity and accuracy will be dealt with quantitatively in the oral presentation. Ideally, an organization should be able to derive its environmental impacts from readily available Enterprise Resource Planning (ERP) data, linking supply chains back to the cradle of resource extraction, excluding the need for an approximation with product group averages. While this study has taken a step in that direction, more research should be conducted especially on how efforts towards sustainable development should be addressed with care to valorise them efficiently in the supply chain and its sectors, beyond any company borders.

At an ever increasing rate innovative chemistry and technology platforms are reshaping manufacturing environments to become factories of the future as being more productive, lean and flexible. The use of Life Cycle Assessment (LCA) in early process development phases has been challenged many times to assess whether or not this willingness to strive for innovation is an environmentally sustainable one. This paper provides general solutions to two major bottlenecks in applying LCA in R&D decision trees; (1) the lack of sufficient process data in early development phases and (2) the lack of knowledge on the effect of scale and learning on the environmental sustainability of a chemical product or process. The first issue is tackled with the use of multiple linear regression modeling, while for the second bottleneck the concept of deriving experience curves is proposed and illustrated. While this paper has taken a step in the direction of including LCA as a decision making tool in early process development phases, it stresses that more research should be conducted especially on Sustainable Supply Chain Management (SSCM), since it makes little sense to optimize in-house production without a proper procurement and Supply Chain Management (SCM).

‘Sustainable development’ is a top priority in the EU. It wants to reduce the footprint of our consumption patterns while assuring sustainable growth and employment in our resources poor continent. Under the ‘UK Climate Change Act’, the NHS (biggest UK health care organization) wants to reduce the carbon footprint with 80% against 2050. Reducing the footprint of medicines is crucial to reach this goal. Mapping the impact of the lifecycle of our products is elementary. But how do we make lifecycle assessments? Which standards exist? What are typical LCA output indicators? Which methods did we use? Why? These and other questions will be answered during this first introductory session to LCA.

After the introductory session on life cycle assessments (LCA), we will run through the most important study results and conclusions of the performed studies. Where are we today? Which blind spots remain? What are the ‘hotspots’ in our analyses and do they confirm our intuition? What is the footprint order of magnitude of a medicine? How can this science contribute to optimise the footprint of our products and services? Will we reach the proposed NHS goal of 80% footprint reduction against 2050? What are realistic scenarios? Which studies do we plan in the near future and why? After this session you’re well placed to help and improve the sustainability profile of our newly developed products.