Expanding the Boundaries: Developing a Streamlined Tool for Eco

Article pubs.acs.org/OPRD

Expanding the Boundaries: Developing a Streamlined Tool for Eco-Footprinting of Pharmaceuticals Concepción Jiménez-González,*,† Caleb Ollech,‡ William Pyrz,§ David Hughes,§ Quirinus B. Broxterman,⊥ and Neil Bhathela∥ †

GlaxoSmithKline, 5 Moore Drive, Research Triangle Park, North Carolina 27709, United States Biomedical Engineering, Duke University, Durham, North Carolina 27708, United States § Merck and Co. Inc., P.O. Box 2000, Rahway, New Jersey 07065, United States ⊥ DSM Innovative Synthesis B.V., Urmonderbaan 22, P.O. Box 18, 6160 MD Geleen, The Netherlands ∥ Environmental Health Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States ‡

ABSTRACT: The American Chemical Society Green Chemistry Institute Pharmaceutical Roundtable has utilized process mass intensity (PMI) as the key high-level metric to evaluate and benchmark progress towards more sustainable manufacturing. The long-term aim is to move towards metrics based on life cycle assessment (LCA) methodologies. This paper describes the development of a streamlined PMI and LCA tool intended to be used as the standard early assessment of synthetic routes for active pharmaceutical ingredients (API) and fine chemicals, especially during the route development stages when life cycle inventory data are often not readily available.

1. INTRODUCTION The pharmaceutical industry is devoted to bringing key medicines to the patient with minimal environmental impact. This is driven by the desire not only to reduce costs but also to increase the sustainability of the manufacturing process. In 2005, the American Chemical Society (ACS) Green Chemistry Institute (GCI) and several global pharmaceutical corporations founded the ACS GCI Pharmaceutical Roundtable (GCIPR or the Roundtable). The activities of the Roundtable reflect the joint belief that the pursuit of green chemistry and engineering is an imperative for making businesses more sustainable with less environmental impact. The Roundtable has translated its belief into a mission that seeks to catalyze the implementation of green chemistry and engineering into the business of drug discovery, development, and production. Several metrics have been proposed under this premise to encourage chemists and engineers to design chemistries and processes that are greener, safer, and more sustainable.1−13 The Roundtable has selected process mass intensity (PMI, the total mass of materials per unit mass of product) as the key massbased green metric. The Roundtable routinely uses PMI to benchmark the greenness of processes and uses it to drive greater efficiency and innovation in the pharmaceutical and fine chemicals industries. The Roundtable presented the rationale behind the selection of PMI as the key mass-based green metric for the pharmaceutical industry in a previous publication.14 In that paper the Roundtable recognized that PMI is not perfect, as it does not provide a holistic life cycle assessment (LCA) view. In addition, PMI does not include specific concerns regarding environment, health, and safety of the materials involved or the waste produced. However, mass metrics such as PMI or its inverse, mass efficiency, are an indispensable intermediate step to estimate LCAs and © 2013 American Chemical Society

footprints. Such metrics can directly be measured by chemists and engineers in laboratory settings with minimum investment in time and effort. This contribution describes the next steps towards achieving a standard LCA measure of pharmaceuticals across the industry. In that context, this research covers the development of a streamlined life cycle assessment tool intended to be used as the standard across the pharmaceutical industry for eco-footprinting synthetic routes in the early stages of development, when very limited life cycle inventory (LCI) data are available. The Rountable’s PMI and LCA tool is based on GlaxoSmithKline’s FLASC tool for LCA and the Roundtable’s PMI tool initially developed at Merck.

2. THE ROUNDTABLE’S PMI CALCULATOR TOOL One of the goals of sustainability, green chemistry, and green engineering is the optimization of resource utilization. This challenge has been recognized by the ACS GCIPR and has resulted in the adoption of process mass intensity (PMI) as the preferred metric aimed to drive greater efficiencies in pharmaceutical syntheses. PMI is defined as the total mass of materials used to produce a specified mass of product (eq 1). Materials include reactants, reagents, solvents used for reaction and purification, and catalysts. Ideally this equals 1 when no waste is produced and all materials are incorporated into the product (eq 1). Another way to express this is in term of efficiency, where mass efficiency is the inverse of PMI. In other words, this is represented as the percentage of the total input mass that is incorporated into the product. Received: October 29, 2012 Published: January 3, 2013 239

dx.doi.org/10.1021/op3003079 | Org. Process Res. Dev. 2013, 17, 239−246

Organic Process Research & Development PMI =

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calculates the step and overall PMI for linear sequences as well as separate PMIs for solvents, water, and reagents. The PMI calculator tool and a short guide can be found at the web site of the ACS GCI Pharmaceutical Roundtable.16 Since launching the PMI calculator tool, the Roundtable has encouraged suppliers to calculate and provide PMI data for all APIs and API intermediates. This would involve all stages of development, including the breakdown for solvents, reagents, and water PMIs.

total mass used in a process or process step(kg) mass of product(kg) (1)

The Roundtable members have used this common PMI metric routinely to compare data from each company on an equivalent basis. In addition to the internal PMI benchmark, in 2011 the Roundtable developed a PMI calculator tool as part of the effort to collaborate with pharmaceutical suppliers to influence more sustainable practices across the pharmaceutical supply chain15 The PMI calculator tool was based on the PMI tool that had been previously developed by Merck. The objective of having an industry-wide PMI calculator was to have a standard methodology and tool to calculate process mass intensity across the entire pharmaceutical supply chain. This would enable consistent and comparable insights on the sustainability of the overall manufacturing process, from bulk chemicals to APIs. In practical terms, the PMI calculator tool consists of a spreadsheet with embedded calculations to facilitate the work of the chemist or engineer estimating PMI (Figure 1). The user

3. MOVING TOWARDS LIFE CYCLE ASSESSMENT Life cycle inventory and assessment (LCI/A) is a methodology that allows one to estimate the cumulative environmental impacts associated with a given process or product across its entire life cycle.17,18 The results of an LCA provide a comprehensive view of the environmental impacts of the product or process with a more accurate picture of the true environmental trade-offs in product and process selection.19 These impacts are often not considered in more traditional analyses. To measure the ‘greenness’ of a process, one would need to have at hand a variety of metrics that include LCI/A metrics to best represent the overall sustainability of a process or product. There is some work that has been performed since the late 1990s on the LCA of pharmaceutical products and processes.20−23 However, performing LCI/A requires significantly more resources than estimating metrics such as PMI; as more materials are used in the process, the more LCI data will need to be collected, verified, and analyzed. Given the labor-intensive nature of traditional LCA, some companies have developed some streamlined methodologies and tools to estimate environmental footprints. For instance, a streamlined LCA methodology has been followed in assessing an API from Hoffmann La-Roche in comparison with the LCA of another API.20 GlaxoSmithKline (GSK) has developed a streamlined methodology and tool called Fast Life cycle Assessment of Synthetic Chemistry tool, or FLASC, which allows for the rapid eco-footprinting of the materials used in the synthetic routes. In GSK’s FLASC a score between 1 (bad) and 5 (good) is estimated using eight different environmental impacts and normalizing for the molecular weight of the API. LCI data gaps are filled using a methodology based on principal component analysis (PCA) and broad categories for materials.24 The first version of FLASC had 14 material categories to fill in the data gaps, which was later simplified to eight categories in a 2010 revision of the principal component and uncertainty analyses. In spite of the advances with company-specific LCA case studies and tools, the application of LCI/A is not a routine practice within pharmaceuticals due to the lack of industry-wide streamlined LCA tools that are easy-to-use, consistent, and transparent. The other gap is the limited LCI information available for materials commonly used in pharmaceutical manufacturing.25 The tool presented in this article is intended to start closing those gaps, as a first step towards an industry-wide standard LCA tool that would inform decisions during product and process design in pharmaceuticals. 4. DEVELOPING THE PMI AND LCA TOOL The PMI and LCA tool is heavily based on the existing PMI calculator tool described above,15 and GSK’s FLASC tool and methodology is described in the literature.24 The operating idea would be to use the existing PMI calculator tool and build into it

Figure 1. Snapshot of the ACS GCI Pharmaceutical Roundtable’s PMI Calculator launched in 2010, showing the second step of a process.

needs only to fill in the amounts of reagents as well as solvents and aqueous materials needed in the route. The spreadsheet 240


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Figure 2. Data entry page for one step.

larger (13.1% and 26.1%, respectively) but still within acceptable range. On the basis of this information, it was decided to preload the known LCA information of solvents and use the same PCAbased categories used in FLASC as a strategy to fill LCA data gaps. The LCA data for reagents and aqueous solutions are then estimated by using the category averages of the FLASC database. The LCA data for commonly used solvents were extracted from the Ecoinvent Database.26 Also, each company using the tool will

LCA calculations similar to the ones used in FLASC. The tool in practice adds a streamlined environmental LCA estimation to the existing PMI calculator. Prior uncertainty analyses performed during the design of FLASC and its subsequent revision show that, for synthetic routes, when the LCI information of solvents is known, the error mass, energy, smog formation, global warming potential, total organic carbon and oil equivalents vary from 4.2 to 6.1%; with the error for acidification and eutrophication being considerably 241



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Table 1. General guidance for classification of materials classification organic complex intermediates

inorganic enzymes and plant extracts renewable feedstock lithium compounds transition metal compounds

guidance This group contains most organic materials used in a typical synthesis, with the exception of natural products and highly complex intermediates (see below). This is defined as an intermediate molecular weight of which (excluding halogens) is above 220 g/mol. There is a very wide variation in the impacts associated with complex intermediates, some of which can be very large. As much as practicable, rather than classify a material in this category every effort should be made to substitute it by the materials and their masses used in its manufacture (all expressed per kg per final product). This category should be used as a last resort. This group contains most inorganic materials used in a typical synthesis, with the exception of lithium compounds and materials containing transition metals. This group contains enzymes and materials obtained from natural products by extraction. This group includes organic materials that can be made from renewable feedstocks, rather than extracted from fossil fuel resources (e.g., glucose, dextrin, corn steep liquor). This group includes any material containing lithium. This group includes any material containing a transition metal cation as determined from the periodic table.

5.2. PMI and LCA Output. The PMI numeric output is the same as the original tool, and it is shown in the calculating page. A visual PMI output tab was added in this version, showing the PMI breakdown in a bar graph for improved presentation. The LCA output is shown in the calculating page per individual compound, per step, and per total synthesis. The LCA estimated impact categories are the following: • mass net - net life cycle mass of materials used (kg/kg API) • life cycle energy - (also known as cumulative energy demand, MJ/kg API) • GWP - global warming potential (carbon footprint, kg of CO2 equiv/kg API) • life cycle water usage, exclusive of process water (water footprinting, kg/kg API) • oil and natural gas depletion for materials manufacture (kg/kg API) • acidification potential (AP, kg of SO2 equiv/kg API) • eutrophication potential (EP, kg of (PO4)−3 equiv/kg API) • photochemical ozone creation potential (POCP, kg of ethylene equiv) • total organic carbon (kg TOC) load before waste treatment There is also a summary output page that shows detailed PMI and LCA metrics per step and cumulative. The metrics in the summary page are also broken down by substrate, reagents, solvents, aqueous reactants and specific LCA impacts. Figures 4 and 5 show respectively the LCA output and the PMI visual output and for the materials of the example shown in Figure 2. 5.3. PMI Information Customization. Since significantly more information is produced as output, the tool has the functionality to show as much or as little detail as needed, depending on the user’s needs and preferences. This is accomplished through a show/hide button.

have the capability to add LCA information if they have the data from internal LCAs and comparisons.

5. PMI AND LCA TOOL FUNCTIONALITY 5.1. Data Requirements. There were no changes to the data requirements, although the input is given in a slightly different fashion to allow for the estimation of LCA information. The user needs to input the materials used within a given step in the synthesis with their respective amounts used (i.e., reagents, aqueous mixes, and solvents). The user also needs to enter key synthesis metrics such as batch size, assay purity, expected product mass, and expected product. Figure 2 shows the data entry page of the tool. To enable the extraction of the appropriate LCA data, the reagents are classified as one of seven distinct categories (complex intermediates, enzymes and plant extracts, inorganics, lithium compounds, organics, renewable feedstocks, or transition metals). These classifications are, in general, intuitive for practicing chemists; however, a general guide on what materials belong to each classification is shown in Table 1, as an adaptation of the guidance previously presented in the literature for FLASC.24 Although there is uncertainty introduced in the calculations for not having individual data per each material, the errors found in the uncertainty assessment were found to be acceptable within the intended use of this streamlined tool, as discussed below under Uncertainty Analysis. Since LCA data for commonly used solvents are available, the user selects a solvent from a drop-down list and inputs the corresponding amount of solvent used. Selecting a solvent automatically fills compound category as “solvent” and extracts the corresponding LCA information for that specific compound. One relevant aspect to highlight is the potential for solvent reuse and recycling. When a solvent is known to be recycled, the assessment can be performed by entering the net solvent used in the synthesis (i.e., gross solvent used minus solvent recoved). Figure 3 shows an example of the filled material data for a twostep synthesis as shown in a simplified form in the following reactions:

6. UNCERTAINTY ANALYSIS The uncertainty of the LCA output of the tool was assessed by comparing the LCA impacts of two sets of 34 linear synthetic routes estimated previously with the results that the tool gave for the same routes. Most of the synthetic routes were taken randomly from FLASC, with the exception of three additional carbon footprint results that were provided by DSM.27 Discrepancies in the resulting LCA data aggregations may be attributed to differences in the databases (FLASC and EcoInvent) from which LCA data were sourced, type of processes used, LCA data gaps, errors on the LCA data or calculation errors. Data from FLASC and DSM were treated as 242



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Figure 3. Example of a two-step synthesis material data input. Substrates are not listed for step 1 because they represent intermediates from the previous step.

the ‘correct’ data set in the uncertainty analysis since significantly more LCA data for reagents and other chemicals are available in the databases. For instance, the FLASC database has LCA data for close to 400 materials used in fine

chemicals and pharmaceuticals. In contrast, the PMI and LCA tool only has LCA data for 61 solvents and uses averages and data for reagents, substrates, and solvents not included in the tool. 243



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Figure 4. Summary table reformatted to exclude blank process steps. The energy figure shown correspond to the total life cycle energy for the manufacture of the materials used.

Table 2. Results of the uncertainty analysis for the two sets of 34 synthetic routes (Groups 1 and 2) mass net (%) Group 1 11.4 Group 2 16.2

life cycle energy (%)

POCP (%)

GWP (%)

acidification potential (%)

eutrophication potential (%)

TOC (%)

oil and gas depletion (%)

11.0

7.7

4.4

10.3

11.8

5.8

1.9

22.5

21.3

13.1

14.4

13.5

23.6

5.2

Uncertainty levels were estimated using the normalized rootmean-square deviation method (NRMSD, reported elsewhere in the literature). These estimations were performed for each impact category (e.g., global warming potential, mass, energy, etc.) for the two sets of 34 synthetic routes. Results of the error analysis performed are shown in Table 2. The two data sets containing the results of 34 routes each are labeled Group 1 and Group 2. Water usage was not analyzed because the assessment in FLASC did not include water footprint until more recently and not enough data were available. In summary, the yielded error percentages ranging from 1.9% to 11.8% for Group 1, and 5.2% to 23.6% for Group 2. NRMSD calculations in summary seem to indicate that the

degree of uncertainty for the PMI/LCA tool seem to fall within a reasonable range for a streamlined LCA tool, given the time for the assessment and the limited data set. Given the close link between energy use and GWP, one point of note is the difference in the variances for these two factors, which may be explained by the green house gas emissions from fermentation processes included in the routes used for the uncertainty analysis. Even though the NRMSD results were promising, further analysis using an unpaired Student t test was performed to determine whether the estimations from the PMI/LCA tool were statistically different from the estimations from FLASC. The t test resulted in the conclusion that the data for global warming 244



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It is important to note that the tool can accommodate the addition of solvent LCA data to augment that already included. Another limitation is that the PMI/LCA tool is only incorporating the supply chain impacts and excludes at this time the process energy for the synthetic route. The pharmaceutical industry historically has not tracked process energy, and thus data in this area are scarce. From full LCA conducted previously, the impacts of supply-chain resources tend to dwarf the impact from the pharmaceutical process energy as a contributor to an API LCA profile. While such an omission may preclude a user from obtaining a complete estimation of an API’s total footprint using this tool, we believe that the gains in the rapid estimation of footprints and the insights gained outweigh this limitation in the first version of the tool.

8. CONCLUSIONS AND FUTURE WORK Simple, but not simplistic, streamlined life cycle assessment tools are still needed to embed sustainability decisions in the design of pharmaceutical products and processes. In this contribution we have presented a standard tool for estimating life cycle assessment information of the materials used to produce active pharmaceutical ingredients. This tool would allow the current Roundtable members and their suppliers to have a quick estimation of the material footprint in a matter of minutes instead of days or months. This would allow the faster incorporation of sustainability principles by design. The uncertainty assessment performed shows that the tool seems to provide an adequate streamlined estimation of the supply chain footprint for net mass, green house gas emissions, eutrophication potential, and fossil fuel depletion. At this point, the tool’s reliability as an estimator of acidification potential and water usage is uncertain and in need of further analysis. LCA data produced with the tool were relatively consistent with that sourced from FLASC, with NRMSD of 1.9−23.6% from one data set to the other, depending on LCA category. These uncertainty ranges are in general very good for a streamlined LCA tool, particularly considering that the time investment is measured in minutes, compared to months for traditional LCA assessments. The PMI and LCA tool presented in this paper has some limitations and it will continue to evolve. As expected from any LCA streamlined tool, there is a necessary trade-off between accuracy, time and effort. We hope to work within the roundtable to incorporate additional LCA data for materials commonly used in pharmaceuticals and fine chemicals, which will help to increase the accuracy of the assessments. Future work is also expected to continue expanding the functionality and usability of the tool, such as the ability to estimate the metrics of convergent syntheses without much manual manipulation or to improve the userfriendliness of the data entry and outcome presentation. The tool could potentially be expanded to estimate LCAs of formulated products by including a final step of formulation and by accounting for overall process and facility energy. The work described here is an extension of the efforts of the ACS GCI Pharmaceutical Roundtable’s strategic goal of delivering tools for innovation.

Figure 5. Summary PMI graphical output reformatted to exclude blank process steps.

potential (GWP), oil/gas depletion, mass and eutrophication obtained from the PMI/LCA tool are not significantly different from those of FLASC in both Group 1 and Group 2 with a confidence level of 95%. Energy, POCP, and TOC were found to be significantly different in both groups, and acidification was found to be statistically different in Group 1, but not in Group 2. Given this assessment, estimations of GWP, oil/gas depletion, mass, and eutrophication given by the tool seem to be the ones with the lower level of uncertainty when using FLASC as a benchmark. The above can be potentially explained by the wide range of values and the limited availability of data, although this would need to be further explored as the LCA data increase and more assessments are performed.

7. LIMITATIONS While the PMI/LCA tool discussed in this paper could be regarded as step forward in the pharmaceutical industry’s ability to evaluate product and process sustainability, it is not without its limitations. While the current tool can calculate PMI data for convergent syntheses, this requires some manual input to include the material requirements of a convergent branch at the appropriate point of the synthesis. This is possible since the LCA estimations are dependent only on the bill of materials and not on the stoichiometric ratios used to determine PMI. Another limitation of course is the existence of data gaps. LCA data are included for only 61 commonly used solvents. There are, of course, common solvents not included in the EcoInvent database (e.g., triethlyamine). Data gaps in reagents are filled by average data based on a PCA study done in GSK. Some ‘nearest neighbor’ approaches could be used to fill data gaps in solvents.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] Notes

The authors declare no competing financial interest. 245



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(15) Broxterman, Q. B.; Hughes, D. L.; Pryz, W. D. Pharma and Suppliers Collaborating on Green Chemistry Launch of PMI Tool. Chim Oggi 2011, 29, 58−61. (16) American Chemical Society, Green Chemistry Institute Pharmaceutical Roundtable website. http://portal.acs.org/portal/acs/corg/ content?_nfpb=true&_pageLabel=PP_TRANSITIONMAIN&node_id= 1422&use_sec=false&sec_url_var=region1&__uuid=14a9778f-4b9b4f93-a163-a1cac2c1e146 (last accessed Aug 7, 2012). (17) National Service Center for Environmental Publications (NSCEP); National Risk Management Research Laboratory (NRMRL. Life Cycle Assessment, Principles and Practice, EPA/600/R06/060; U.S. Environmental Protection Agency: Cincinnati, OH, May 2006. (18) Wenzel, H.; Hauschild, M.; Alting, L. Methodology, Tools and Case Studies in Product Development. Environmental Assessment of Products; Chapman and Hall: New York, 1997; Vol. 1. (19) Society of Environmental Toxicology and Chemistry (SETAC). A Conceptual Framework for Life Cycle Impact Assessment; Consoli, F., Denison, R., Dickson, K., Mohin, T., Vigon, B., Eds.; SETAC Foundation for Environmental Education Inc.: Pensacola, FL, 1993; p 105. (20) Wernet, G.; Conradt, S.; Isenring, H.; Jimenez-Gonzalez, C.; Hungerbuhler, K. Life Cycle Assessment of Fine Chemical Production: A Case Study of Pharmaceutical Synthesis. Int. J. Life Cycle Assess. 2010, 15, 294−303. (21) Jimenez-Gonzalez, C.; Curzons, A. D.; Constable, D. J. C.; Cunningham, V. L. Cradle-to-Gate Life Cycle Inventory and Assessment of Pharmaceutical Compounds. Int. J. Life Cycle Assess. 2004, 9, 114−121. (22) Henderson, R.; Jiménez-González, C.; Preston, C.; Constable, D. J. C.; Woodley, J. M. EHS and LCA Assessment for 7-ACA synthesis: A Case Study for Comparing Biocatalytic and Chemical Synthesis. Ind. Biotechnol. 2008, 4, 180−192. (23) Jiménez-González, C. Life Cycle Assessment in Pharmaceutical Applications. Ph.D. Thesis; North Carolina State University: Chapel Hill, NC, 2000. (24) Curzons, A. D.; Jimenez-Gonzalez, C.; Duncan, A.; Constable, D. J. C.; Cunningham, V. L. Fast Life-cycle Assessment of Synthetic Chemistry Tool, FLASC Tool. Int. J. Life Cycle Assess. 2007, 12, 272− 280. (25) Jimenez-Gonzalez, C.; Poechlauer, P.; Broxterman, Q. R.; Yang, B. S.; am Ende, D.; Baird, J.; Bertsch, C.; Hannah, R. E.; Dell’Orco, P.; Noorman, H.; Yee, S.; Reintjens, R.; Well, A.; Massonneau, V.; Manley, J. Key Green Engineering Research Areas for Sustainable Manufacturing: A Perspective from Pharmaceutical and Fine Chemicals Manufacturers. Org. Process Res. Dev. 2011b, 15 (4), 900−911. (26) EcoInvent Database, V2.2; Swiss Centre for Life Cycle Inventories: St. Gallen, Switzerland, 2010. (27) Hermsen, P. E-mail communication. July 23rd, 2012.

ACKNOWLEDGMENTS We thank all the companies of the ACS GCI Pharmaceutical Roundtable that have participated in the PMI benchmark over the years. We want to thank the following members of the roundtable for beta-testing the PMI and LCA tool and providing very useful feedback for usability and uncertainty assessment: Caireen Hargreaves (Astra Zeneca), Ingrid Mergelsberg (Merck), Peter Hermsen (DSM), Sandy Yee-Lee (Johnson & Johnson), Michal Justus (Johnson & Johnson), and Fabrice Gallou (Novartis). Special thanks go to the colleagues within GlaxoSmithKline who devised and delivered FLASC: Alan Curzons, David Constable, Ailsa Duncan, Virginia Cunningham; and the ones who collaborated in reviewing it: Rebecca DeLeeuwe, Daniel Castro, Celia Ponder, and Richard Henderson. Additional thanks go to Celia Ponder for her guidance and insights on the LCI databases and LCA concepts provided to the summer interns working on the project.

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Expanding the Boundaries: Developing a Streamlined Tool for Eco

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