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Cradle-to-gate Greenhouse Gas Emissions for Twenty Anesthetic Active Pharmaceutical Ingredients based on Process Scale-up and Process Design Calculations Abhijeet Parvatker, Huseyin Tunceroglu, Jodi D Sherman, Philip Coish, Paul T. Anastas, Julie B. Zimmerman, and Matthew J. Eckelman ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.8b05473 • Publication Date (Web): 20 Jan 2019 Downloaded from http://pubs.acs.org on January 21, 2019

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ACS Sustainable Chemistry & Engineering

Title: Cradle-to-gate Greenhouse Gas Emissions for Twenty Anesthetic Active Pharmaceutical Ingredients based on Process Scale-up and Process Design Calculations

Authors: Abhijeet G. Parvatker1, Huseyin Tunceroglu2, Jodi D. Sherman2, Philip Coish3, Paul Anastas3,4, Julie B. Zimmerman3,4,5, Matthew J. Eckelman6* 1Department

of Chemical Engineering, Northeastern University, 313 Snell Engineering Center, 360 Huntington Ave, Boston, MA 02115, United States 2Department

of Anesthesiology, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520,

United States 3Center

for Green Chemistry and Green Engineering at Yale, 225 Prospect Street, New Haven, CT 06520, United States 4School

of Forestry and Environmental Studies, Yale University, 370 Prospect Street, New Haven, CT 06511, United States 5Department

of Chemical & Environmental Engineering, Yale University, 9 Hillhouse Avenue, New Haven, CT 06511, United States 6Department

of Civil and Environmental Engineering, Northeastern University, 400 Snell Engineering Center, 360 Huntington Ave, Boston, MA 02115, United States *Corresponding Author: [email protected], Tel: +1 617 373 4256; Fax: +1 617 373 4419

Keywords: Chemicals, Life Cycle Inventory, Sustainability, Healthcare

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Abstract Comparative life-cycle assessment (LCA) of pharmaceutical drugs would enable clinicians to choose alternatives with lower environmental impact from options offering equivalent efficacies and comparable costs. However, life-cycle inventory (LCI) data of individual pharmaceutical drugs is limited to only a few compounds. In this study, we use chemical engineering methods for process scale-up and process design to utilize lab-scale synthesis data, available in patents and other public literature, to generate cradle-to-gate LCI data of 20 commonly used injectable drugs in anesthesia care to calculate their greenhouse gas impact. During the process of building the life-cycle trees of these drugs, missing life-cycle inventories for more than 130 other chemical compounds and pharmaceutical intermediates were accounted for using process-based methods and stoichiometric calculations. The cradle-to-gate GHG emissions of the 20 anesthetic drugs range from 11 kg CO2 eq. for succinylcholine to 3,000 kg CO2 eq. for dexmedetomidine. GHG emissions are positively correlated with the number of synthesis steps in the manufacturing of the drug. The LCI methods and data generated in this work greatly expand the available environmental data on APIs and can serve as a guide for LCA practitioners in future analysis of other pharmaceutical drugs. Most importantly, these LCA results can be used by clinical practitioners and administrators building toward sustainability in the health care sector.


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Introduction The United States health care sector contributes 9-10% of the total national greenhouse gas (GHG) emissions, approximately 10% of which are attributed to the manufacturing of prescription drugs.1 Similar studies conducted in Australia, United Kingdom, and Canada determined that pharmaceutical products account for 19-25% of total GHG emissions of the public health services for those countries.2– 4

These studies relied on a top-down methodology, Environmentally-Extended Input-Output (EEIO)

modelling, which associates monetary expenditures for certain goods or services with corresponding emission intensity values, in order to calculate economy-wide emissions stemming from those expenditures .1,2,5 While this top-down approach has allowed for estimation of the overall contribution of pharmaceutical products to the GHG emissions of the healthcare sector, the results are aggregated across all drugs and give no information on the carbon footprint or other environmental impacts associated with individual pharmaceutical products. At the individual compound level, there are very few life cycle inventories (LCI) for pharmaceutical drugs and their intermediates available in academic or industry literature in public domain. Such LCI data are necessary to perform bottom-up processbased life cycle assessments (LCA).6–8 As a result, health care providers and administrators lack actionable information to guide environmentally preferable purchasing or changes to clinical practice that could reduce health care sector impacts.9,10 Furthermore, such information is critical for improving process design for production of active pharmaceutical ingredients (APIs) in order to reduce resource use and environmental impacts.11,12 An API can be defined as, “Any substance or mixture of substances intended to be used in the manufacture of a drug (medicinal) product and that, when used in the production of a drug, becomes an active ingredient of the drug product. Such substances are intended to furnish pharmacological activity or other direct effect in the diagnosis, cure, mitigation, treatment, or prevention of disease or to affect the structure and function of the body”. 13 API manufacturing typically involves multi-step chemical syntheses and unit operations such as purification, crystallization, and drying followed by operations for packaging, labeling, and testing.14 Manufacturing of APIs is known to be among the most energy and materials-intensive in the chemicals sector, and there has been increasing interest in evaluating the environmental sustainability of pharmaceutical manufacturing.15

Background Life cycle assessment (LCA) is a widely-used systems modeling tool designed to quantify the environmental impacts associated with all the life cycle stages of a product, from the raw material extraction to its end-of-life. LCA practice has been codified in the ISO standards 14040-44.16 Life cycle inventory (LCI) is a step in the LCA that consists of detailed tracking of all the flows in and out of the



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product system, including raw resources or materials, energy by type, water, and emissions to air, water and land by specific substance.16 The life cycle impact assessment (LCIA) step then uses coupled fate-exposure-effect models to link these flows to various categories of environmental and health impacts, which can serve as metrics for comparing the environmental sustainability of products and services. The use of LCA is critical to identifying and devising strategies to reduce or eliminate environmental “hot-spots” in the product life cycle or to make informed decisions between product alternatives. While LCA has seen limited implementation within the pharmaceutical industry,6,17 primarily for process intensification,11,12,18 solvent waste management, and end-of-life treatment,19–23 the lack of life cycle inventory data is identified as one of the major hurdles in conducting comprehensive LCAs for individual pharmaceutical products.8,15,24 This dearth stems from strict intellectual property protections of API production processes given the high value of the products.15 This gap in knowledge also applies to many of the chemical intermediates used in producing APIs, which are often complex molecules that can require dozens of synthesis steps, further increasing the difficulties in building pharmaceutical LCIs.25 As such, pharmaceutical LCA efforts have largely been limited to individual APIs for which pharmaceutical companies have provided process data;6,8,24 still in many cases, the products have not been named,26–28 limiting the value of the results in decision-making. Some leading pharmaceutical companies are also adopting green chemistry metrics and LCA-based tools for internal use,26,29,30 such as the Fast Life Cycle Assessment of Synthetic Chemistry (FLASCTM) tool developed by pharmaceutical company GlaxoSmithKline (GSK).31 As the problem of limited LCIs is widespread across the chemicals industry, several methodologies for estimation of chemical LCIs have been proposed,32–34 including for pharmaceuticals,24,28 depending on the types of data available. Most common are stoichiometric approaches based on the basic chemical reactions of the synthesis, and statistical or machine-learning models where only the target compound is known.35,36 For example, FineChem is a molecular structure-based model which uses molecular descriptors to predict the environmental impacts of API production through Artificial Neural Networks.35 However, the applicability of such models is limited by the small training dataset covering specific chemical classes,35,36 and with so few existing LCIs for APIs, such tools are not appropriate to use for pharmaceutical LCA. Moreover, such models only predict life cycle impact assessment results, and cannot be used to generate the process LCI data required to perform location-specific assessment or inform process design. In addition to LCA, several other green chemistry metrics have been considered in evaluating the sustainability of APIs production.37–39 These are typically mass-based metrics that quantify the 4 ACS Paragon Plus Environment

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efficiency of the reaction and reduction of waste and hazardous substances during synthesis. For instance, atom economy is the ratio of molecular mass of the reactants to the molecular mass of the product based on the stoichiometry of the reaction, and is a useful method for comparing synthesis options for their mass efficiency.40 E-factor is another popular metric for assessing the environmental impacts and is simply defined as kg waste generated/kg desired product,41 with low scores being desirable. Whereas the E-factor (E) for the oil refining industry ~0.1, the E-factor for pharmaceutical manufacturing could be as high as 100, indicating a high waste-to-product ratio.41 Other metrics include effective mass yield (EMY),42 which is defined as the ratio of desired product to the non-benign reactants used in the synthesis, and process mass intensity (PMI), which is the ratio of total mass of material used to the mass of desired product. While such green chemistry metrics are widely used in the pharmaceutical industry,37,43 their utility for systems-level environmental assessment is limited because they track only mass flows and are often restricted to the last step(s) of chemical synthesis rather than assessing the entire synthesis chain. As Roger Sheldon, who developed the E-factor metric, argues, these metrics are useful indicators of process efficiency, but are not suitable to quantify the actual impacts of a process or target product on the environment, which would be possible with methodologies like LCA.44

Objective and scope We previously used LCA to describe the life cycle GHG emissions of clinically equivalent doses of general anesthetic drugs: four common inhaled drugs, which are themselves greenhouse gases, as well as the alternative intravenous drug, Propofol. Total life cycle GHG emissions for all inhaled general anesthetic drugs were found to be several orders of magnitude higher than that of Propofol,10 with the vast majority of impacts resulting from direct venting of waste anesthetic gas post-use, rather than from drug manufacturing. These results have informed the development of recommendations by the American Society of Anesthesiologists Environmental Task Force for provider selection of environmentally-preferable anesthetics when clinically appropriate, as well as waste reduction strategies.9 However, the delivery of comprehensive anesthesia care routinely requires administration of several different drugs, beyond these general anesthetics, and thus recommendations are currently limited. In this study we greatly expand the breadth of LCIs for APIs by making available inventory data for 20 injectable drugs and more than 100 intermediate chemicals and solvents used in the pharmaceutical industry. Unlike LCIs based on primary data from manufacturers, material and energy flows for the LCI are developed based on synthesis data derived from patents and academic and industry literature. Rather than relying just on basic stoichiometric approaches, we used detailed process calculations 5 ACS Paragon Plus Environment


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based on chemical engineering principles and process scale-up methodologies for estimating material and energy requirements for production of APIs. The use of a consistent methodology allows for comparison among anesthetic drug alternatives. The objective of this study is to provide cradle-to-gate greenhouse gas emissions data for a comparative analysis of common anesthetic drugs used with or as alternatives to the inhaled anesthetics considered in a previous study.10 Direct material inputs and energy use are estimated for each gate-to-gate step in the synthesis chain. Due to limited public information on specific plant configurations for pharmaceutical manufacturing, fugitive emissions and disposition of waste solvents and wastewater have not been specified. All the background processes for energy and precursor chemicals have been used from the ecoinvent database v3.2.45 Further, the life-cycle GHG emissions analysis did not include formulation, packaging, distribution, use, excretion or discard of unused drug at end-of-life. The following section describes the general methodology for generating the LCI data, using the synthesis of lidocaine for illustration. Details of all other APIs and chemical intermediates, including reaction schema, process flow diagrams, calculation steps, and full LCI results are provided in the Supporting Information (SI).


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Methods Selection of intravenous drug alternatives We selected 20 injectable drugs commonly used in the delivery of anesthesia care,46,47 shown in Table 1. This list includes several drug classes/subclasses: local anesthetics, sedatives, analgesics including opioids, pressors, anti-hypertensives, muscle relaxants and reversal agents, antiemetics, and antiinflammatories. This breadth was intended to enable comparison of drug alternatives across a wide range of clinical care pathways ranging from analgesia, sedation, local and regional anesthetic approaches, as well as for balanced general anesthetics. Table 1: List 20 anesthetic drugs assessed in the study by drug classes. API Lidocaine Ropivacaine HCl Bupivacaine HCl Propofol Remifentanil Fentanyl Morphine Hydromorphone Midazolam Ketamine Dexmedetomidine Succinylcholine Rocuronium bromide Neostigmine methylsulfate Sugammedex Glycopyrrolate Phenylephrine HCl Ephedrine hydrochloride Epinephrine Ondansetron

Drug class local anesthetic: amide (short acting) local anesthetic: amide (long acting) local anesthetic: amide (long acting) general anesthetic: GABA modulator analgesic: opioid (ultra-short acting) analgesic: opioid(short acting) analgesic: opioid (long acting) analgesic: opioid (long acting) sedative-anxiolytic: benzodiazepine sedative-hypnotic and analgesic: NMDA antagonist sedative-hypnotic and analgesic: selective alpha2 agonist skeletal muscle relaxant-depolarizer (short acting) skeletal muscle relaxant: non-depolarizing (long acting) reversal of non-depolarizer muscle relaxant: cholinesterase inhibitor reversal of non-depolarizer muscle relaxant: cyclodextrin anticholinergic: quaternary amine sympathomimetic: selective alpha1 agonist sympathomimetic: adronergic agonist sympathomimetic: non-selective adronergic agonist antiemetic: 5HT3 antagonist

Methods for API synthesis LCI calculations A summary of the overall method is provided here with the step-by-step details along with different approaches used for the LCI generation. These methods are illustrated in the following section with an example of LCI calculations for lidocaine. In general, a bottom-up, process-based approach was used to generate LCI data for each of the drugs listed in Table 1.33,48 First, the synthesis procedure for each of the 20 drugs was gathered from patents, academic literature, and industry reports and compiled into a reaction scheme. Often, synthesis and purification details 7 ACS Paragon Plus Environment


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were provided for laboratory-scale settings and equipment. In such cases, each laboratory-scale step was then replaced by an equivalent industrial-scale unit operation, which were then integrated into a full process flow diagram (PFD) used in subsequent energy calculations (see following section). For example, the laboratory-scale procedure of evaporating solvent in a rotovap was replaced by an industrial scale rotary dryer.48 Material inputs in the process including reactants solvents, and process aids were obtained from the synthesis steps and scaled-up linearly. Energy inputs were calculated from process flow diagrams using mass- and energy-balance equations and unit empirical models of individual operation, described in detail in Table 2. Following common practice, catalysts are not considered in the inventory calculations because they are commonly unspecified, and can be regenerated and reused, hence their effect on GHG emissions impact would be relatively low compared to other chemical inputs.49 Ideally, however, catalyst input and treatment on spent catalyst would be included in chemical LCIs.50 Solvent recovery is considered for all the life cycle inventories generated using scale-up and process calculation methods. Solvent use accounts for the majority of the mass input in pharmaceutical processes and can be as high as 80-90%,51 accounting for 50% of cradle-to-gate post-treatment GHG emissions.52 Amount of waste solvent recovery depends on several factors such as the composition of the waste mixture, type of solvent, and the type of operation used (batch or continuous).53 A conservative estimate of 80- 95% are assumed for solvent recovery in the LCI calculations based on the amount of solvent used in the synthesis of 20 APIs. This has been done to account for high solvent use in laboratory scale operations compared to lower, optimized use at industrial scales. The solvent use and recovery rates can be updated to represent actual plant scenarios when more information is available. The balance solvent is quantified as an output in the unit LCIs provided in the SI. In a chemical facility with multiple product outputs, this could be a part of an organic feed stream to a thermal oxidizer (TO) for generating energy, or this could be treated by a third-party. Credits and burdens of auxiliary units like TO and pollution control units like scrubbers and waste-water treatment plants have not been considered. These could be considered in a detailed LCA for each of these APIs when more information at the plant level is available. Fugitive emissions have also not been included as part of the LCIs as these would not typically affect the cradle-to-gate GHG emissions results, and because of the lack of actual industrial process data available for pharmaceutical manufacturing. Where plant configurations and reaction products are well-characterized, fugitive emissions could be calculated based on the methodology presented by Smith et al.54 Even for non-GHG environmental impacts, process energy use has been found to account for more than 50% and in some cases up to 80% of the total across a range of chemical products.55 8 ACS Paragon Plus Environment

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When chemical intermediates needed for the synthesis of the target APIs did not themselves have existing LCI data, these were also modeled, working back in a series of steps until all chemical inputs could be matched with existing datasets in the ecoinvent LCI database.56 All LCI data were then entered in the commercial LCA software SimaPro v8.1 (PRé Consultants, Amersfoort, Netherlands), which was used to calculate the system-wide life cycle inventory of raw material inputs and emissions, including emissions of greenhouse gases. Here we report global warming results in carbon dioxide equivalents (CO2e) calculated based on IPCC 2013 GWP100a values,57 though the same LCI data could be used to evaluate other environmental impact categories of interest. Following sub-sections describe the different methods used in LCI generation. The scale-up method with process calculations was used for all of the final steps and for key precursors of the 20 drugs. While the process design calculations and stoichiometric methods were used for chemical intermediates where detailed synthesis procedures were not available. Finally, method of using proxy LCI data for chemicals with minimal synthesis information was used rather than completely omitting the input from the LCI.50 Table 2: Equations used to calculate energy requirements of unit process and operations Unit process/operation Reactor (1000 L) Distillation

Equations used 𝑄𝑟𝑒𝑎𝑐𝑡 = {[𝑚𝑅𝑀 . 𝐶𝑝𝑅𝑀 ∆𝑇 + 𝑚𝐸𝑆 ∆𝐻𝑣𝐸𝑆 ] + [3.303. ∆𝑇. 𝑡]}

(1)

Piccinno et al. 34

(2)

Piccinno et al. 34

(3)

Piccinno et al. 34

𝑡2 − 𝑡1 (3.99 + 0.001939𝑇) 𝑇

(4)

Melpolder et al.58

(𝑚𝐹 𝐶𝑝𝐹 (𝑇𝑏 − 𝑇) + 𝑚𝐸𝑆 ∆𝐻𝑣𝐸𝑆 ) 𝜂ℎ𝑒𝑎𝑡

(5)

Piccinno et al. 34

𝑄𝑑𝑖𝑠𝑡 =

𝑙𝑜𝑔 𝛼 =

𝑄𝑑𝑟𝑦 =

𝑚𝐹 𝐶𝑝 ∆𝑇 + 𝑚𝐷 ∆𝐻𝑣 (1.3𝑅𝑚𝑖𝑛 + 1) 𝜂ℎ𝑒𝑎𝑡 1 𝑋𝐿𝐷 𝛼(1 − 𝑋𝐿𝐷 ) − ( ) 𝛼 − 1 𝑋𝐿𝐹 1 − 𝑋𝐿𝐹

𝑅𝑚𝑖𝑛 =

Drying

References

Crystallization

𝑄𝑐𝑟𝑦𝑠𝑡 = 𝑚𝐹 𝐶𝑝 ∆𝑇 + 𝑚𝑐 ∆𝐻𝑐 − 𝑚𝐸𝑆 ∆𝐻𝑣𝐸𝑆

(6)

Ulrich et al.59

Filtration

𝐸𝑓𝑖𝑙𝑡 = 0.01 𝑘𝑊ℎ/𝑘𝑔 𝑝𝑟𝑜𝑑𝑢𝑐𝑡

(7)

Piccinno et al.34

Pumping

𝐸𝑝𝑢𝑚𝑝 = 1.53 × 10−5𝑚𝑙𝑖𝑞. (𝑘𝑊ℎ⁄𝑘𝑔)

(8)

Piccinno et al.34

Stirring

𝐸𝑠𝑡𝑖𝑟(1000 𝑙) = 0.0180𝜌𝑚𝑖𝑥 𝑡 (𝑚5⁄𝑠 −3)

(9)

Piccinno et al.34

Vacuum

Rotary Piston Vacuum Pumps: 𝑃𝑜𝑤𝑒𝑟 = 4.242 × (𝑆. 𝐹)1.088 (𝐵𝑘𝑊) (𝑆. 𝐹) = 0.03 − 8 𝐴𝑖𝑟 𝑒𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒 (𝑘𝑔⁄ℎ) 𝑆𝑖𝑧𝑒 𝐹𝑎𝑐𝑡𝑜𝑟, 𝑆𝐹 = 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑝𝑟𝑒𝑠𝑠𝑢𝑟𝑒 (𝑚𝑚 𝐻𝑔)

(10)

IPS 199360

Notation: 𝑄𝑟𝑒𝑎𝑐𝑡 , 𝑄𝑑𝑖𝑠𝑡 , 𝑄𝑑𝑟𝑦 , 𝑄𝑐𝑟𝑦𝑠𝑡 – Heat required for reactor, distillation, dryer and crystallizer in MJ 𝑚 – mass of the chemical species (reaction mass or solvent) in kg



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𝑅𝑀, 𝐸𝑆, 𝐹 – Reaction mass, Evaporated solvent, Feed ∆𝑇 – Temperature difference, °C ∆𝐻𝑣 – Enthalpy of vaporization in MJ ∆𝐻𝑐 – Enthalpy of crystallization in MJ 𝜂 – Efficiency 𝑅𝑚𝑖𝑛 – Minimum reflux ratio 𝛼 – Relative volatility 𝑋𝐿𝐷 – Mole fraction of light key in distillate 𝑋𝐿𝐹 – Mole fraction of light key in feed (𝑡2 − 𝑡1 ) – Boiling point difference of separating mixtures, °C 𝑇 – Boiling point of mixture 𝐶𝑝 – Specific heat capacity, kJ/mol.K 𝐸𝑓𝑖𝑙𝑡 – Electricity for filtration, kWh 𝐸𝑝𝑢𝑚𝑝 – Electricity for pumping, kWh 𝑚𝑙𝑖𝑞. – Mass of liquid transferred, kg 𝐸𝑠𝑡𝑖𝑟 – Electricity for stirring, kWh 𝜌𝑚𝑖𝑥 – Density of mixture, kg/m3 𝑡 – time

Scale-up from laboratory processes In absence of publicly available data for production of the anesthetic drugs, patents were used for obtaining their synthesis routes. The processes in these patents are at the lab-scale. While, there have been several studies which use process calculation for calculating the gate-to-gate energy consumption of chemical production,32,48,61,62 the use of process scale-up techniques to address the differences between a lab-scale material and energy use to that of an industrial scale is limited. 34 Piccinno et al.34 provide a general framework for scale-up procedure in life-cycle inventory data generation, using traditional process design based calculations. However, each chemical sub-sector is unique and requires specialized approach in order to convert the data available at lab-scale to represent its applicability at the industrial scale. Pharmaceutical production is done predominantly by batch operations and, unlike in commodity petrochemical manufacturing, is relatively small in scale.28,63 Jacketed batch reactors are used with utilities such as steam and cooling water to maintain the reaction temperature. 64 Typical separation operations used in pharmaceutical production include filtration, distillation, drying and crystallization. Electric drives are used for compressors, transfer of fluid using pumps, stirrers and vacuum pumps. All process vessels were assumed to be 1000 liters in volume, hence corresponding values for surface area, heat transfer coefficients, impeller diameter and rate of heat loss were used. 34 A detailed list of lab-scale steps and their corresponding industrial-scale process along with the assumptions made for the LCI calculation for the unit operation are given in Table 3. This method was used for 84 of the total LCIs generated in this work. Process design/stoichiometry and proxy/omission methods (detailed in the following sections) were employed when the reactants required in producing the raw materials for the next step were not found in existing LCI databases. Detailed calculations for all chemicals considered are available in the SI. 10 ACS Paragon Plus Environment

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Table 3: Equivalent industrial-scale operation for the lab-scale step in the synthesis and assumptions for LCI calculation for each unit operation Unit operation

Lab-scale step

Industrial operation

Assumptions

Reaction with mixing, heating or cooling

mix reactants in volumetric flask, add reactant to a mixture slowly

Stirred jacketed batch reactor

• • •

Catalysts are not included in inventory analysis Chemical and physical properties at standard temperature and pressure 1000 liter volume reactor considered for heat loss calculations

Distillation

Distillation, evaporation, solvent removal (follows reaction)

Batch distillation, jacketed vessel

• • •

99% light key product recovered in distillate Relative volatility is estimated based on temperature difference of mixture constituents Only reboiler energy and heating requirements are considered

Filtration

wash, filter

Agitated Filter

•

Mass of final product considered as filtered mass in Equation 7

Drying

Vacuum drying, Rotary evaporation (rotovap), solvent removal (follows filtration)

Vacuum dryer

•

It is assumed that 20% of the solvent from the crystallization and filtration step reaches dryer and 100% of the solvent in the dryer is evaporated Only specific heat capacity of the solvent is considered, sensible heat for heating product is ignored It is assumed that this heat lost is incorporated in the dryer efficiency along with heat lost to the environment

• •

Solvent recovery

Not included

Distillation

•

80-95% solvent recovery assumed to offset the higher amounts of solvents used at lab-scale

Transfer of liquids

Manual

Pumping

•

It is assumed that all liquid mass is transferred using pump once and total mass is used in Equation 8



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Process design calculations and stoichiometry Process design calculations and stoichiometric methods were used when detailed laboratory descriptions for intermediate chemicals were not available to use the scale-up method described in the previous section. In process design calculations, the same equations shown in Table 2 are used for calculating the energy requirements. Material inputs from synthesis steps were scaled to per kg of final product. Stoichiometric methods were used when only the reaction involved in the production of the chemical being investigated was known, without any further process description. Hence, the reaction conditions, separation operations and auxiliary units used in the process are unknown. However, an effort is made to estimate the energy requirements of the process based only on the heat of reaction. Heat of reaction is the heat released (exothermic) or heat absorbed (endothermic) when the reaction is carried out, and can be calculated as shown in Equation 11. Based on the heat of reaction and the enthalpy of the product and the feed stream, the energy required in form of cooling or heating can be calculated using Equation 12 as shown below.65

0 ∆𝐻𝑟0 = ∑ ∆𝐻𝑓,𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑠 − ∑ ∆𝐻𝑓,0 𝑟𝑒𝑎𝑐𝑡𝑎𝑛𝑡𝑠

(11) where, ∆𝐻𝑟0 – standard heat of reaction ∆𝐻𝑓0 – standard heats of formation (12)

𝑄𝑝 = 𝐻𝑝𝑟𝑜𝑑𝑢𝑐𝑡 − 𝐻𝑓𝑒𝑒𝑑 − 𝑄𝑟 Where, 𝑄𝑝 – Heat required to maintain the design reactor temperature 𝐻𝑝𝑟𝑜𝑑𝑢𝑐𝑡 – Total enthalpy of the product stream 𝐻𝑓𝑒𝑒𝑑 – Total enthalpy of the feed stream 𝑄𝑟 – Total heat generated by the reaction taking place and is given as, 𝑄𝑟 = ∑ −∆𝐻𝑟0 × (𝑚𝑜𝑙 𝑜𝑓 𝑝𝑟𝑜𝑑𝑢𝑐𝑡 𝑓𝑜𝑟𝑚𝑒𝑑)

(13)

If Qp in Equation 12 is negative then the reaction would require cooling, while a positive value indicates that the reaction requires heating to maintain the design temperature. These calculations for the energy requirements of the process may not be complete, nevertheless, it acts as a placeholder until 12 ACS Paragon Plus Environment

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further detailed calculations can be performed on the LCI of these chemicals when their input in the synthesis is more significant.

Proxy LCI data One of the other strategies often used to address the missing LCI data is to use LCI data from a proxy chemical rather than completely omitting the LCI from the analysis.66,67 This type of substitution of the LCI however needs to be done with a clear understanding of the source and manufacturing routes of proxy chemical being used and the one that is being replaced. Often, even if the chemicals sound similar or have same functional groups the production routes and hence the raw materials and the energy requirements could be completely different. For example, cyclohexanol and cyclohexanone, which are basically cyclohexane rings with one hydrogen atom replaced by hydroxyl and ketone functional group respectively, have 30% difference in their cradle-to-gate GHG emissions based on their LCIs in ecoinvent database.68 Similarly, while isobutanol and ethanol are both alcohols, the GHG emissions of the two chemicals vary by 55%, because the precursors for these chemicals are completely different. The proxy chemicals used in this study are most frequently direct precursors to the chemicals with missing LCI data. For example, LCI for phenol is used in place of LCI for thiophenol used in the morphine synthesis, aniline is used since the LCI data for 4-chloroaniline required in the production of Midazolam were not available. Using precursors as proxy reduces the risk of grossly misrepresenting the LCI for the missing chemical since it covers a part of the upstream for the required LCI. Using proxy LCI data were only considered when the data required to use the other methods were not found in patent literature from google search, process literature and synthesis databases such as PrepChem .69 This option was also considered when the mass of the input was less than 5% of the total mass input in the LCI. Hence, the impact of the missing chemicals would be significantly low compared to the final results of the anesthetic LCI. A detailed list of these chemicals is presented in the SI.

Background LCI energy datasets Chemical production requires heating and cooling in the different unit processes and operations, as well as electricity for pumps, stirrers, and other electric drives. For heating, LCIs available in the ecoinvent database have been used for making the datasets for the chemicals in SimaPro v8.1. For steam, ecoinvent unit dataset for steam for chemical processes, in which 76% of the heat is generated from natural gas and the rest from heavy fuel oil is used. Steam is primarily used to represent the heating in solvent recovery operations. While for other process heating, ‘heat, unspecific, in chemical plant’, which represents an arithmetic mean of 215 steam plants is used.68 For process cooling, there are no specific datasets available in the ecoinvent database. Hence, a LCI dataset for cooling water 13 ACS Paragon Plus Environment


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and refrigeration was created based on the work of Jiménez-González.6 For electricity, a medium voltage consumer-mix dataset is used which accounts for the transmission infrastructure and losses, to represent the upstream impacts of the electricity available to chemical plants. Table 4 presents a summary of energy LCI data used and their sources. Table 4: LCI unit process data used for energy use in the LCA for anesthetic drugs. Activity Electricity

LCI data Electricity, medium voltage, consumer mix, at grid/US US-EI U

Source Ecoinvent v3.045

Heat Steam Cooling water Refrigeration

Heat, unspecific, in chemical plant/US- US-EI U Steam, for chemical processes, at plant/US- US-EI U Water make-up, energy use and emissions Refrigerant make-up, energy use and emissions

Ecoinvent v3.045 Ecoinvent v3.045 Jiménez-González6 Jiménez-González6

Example - Lidocaine synthesis LCI calculations This section illustrates the steps used to generate LCI data for lidocaine. Lidocaine is one of the oldest local anesthetic drugs70 and is listed in the essential medicines list published by the World Health Organization (WHO).71 The synthesis of lidocaine from aniline and ethanol in three reaction steps is shown in reaction scheme below (rxn 1-3).

(rxn 1)


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(rxn 2)

(rxn 3) The laboratory synthesis for lidocaine72 was converted into an industrial-scale process by substituting lab-scale operations with those used in the industry, as described in Table 3. This industrial-scale process is represented by the process flow diagram shown in Figure 1.



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Figure 1: Process Flow Diagram (PFD) for synthesis of lidocaine The first step in the synthesis procedure is the reaction between the 2,6-dimethylaniline and chloroacetyl chloride, in the presence of potassium carbonate, with acetone used as the solvent. The reaction occurs for 3 hours at room temperature; hence, no heating is considered for the process. Stirring energy is calculated based on Equation 9 shown in Table 2, used for all stirred batch vessels. Filtration is then used to separate the intermediate choloroacetyl-2,6-dimethylaniline at a yield of 94% from the unreacted reagents. In the next step, the second reactor shown in Figure 1, choloroacetyl2,6-dimethylaniline is reacted with diethylamine under reflux for 8 hours. Pure lidocaine is obtained after a series of separation and purification steps which include filtration, recrystallization and drying. The recrystallization operation is done in a jacketed stirred vessel while a vacuum dryer is operated for 6 hours to obtain the final product with a yield of 99%. A total of 7.02 kg acetone is used as solvent in the two step reaction for producing 1kg lidocaine. The energy calculations for these operations are done using Equations 5-7 from Table 2. The mass inputs are obtained from the synthesis procedure and are linearly scaled resulting in input of 1.13 kg 2,6-dimethylaniline and 1.58 kg chloroacetyl chloride for per kg production of lidocaine. It is assumed that 80% of the solvent used will be recovered by distillation and recycled.53 1.53 kg steam per kg waste solvent recovered is assumed based on the results of an empirical study by Capello et al.53 The chemical and physical properties and operating parameters for calculating the energy requirements of the process and the results for lidocaine are given in Table 5.


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Table 5: Calculations for process energy requirements for production of Lidocaine Product

Lidocaine

Unit operation Dryer (Equation 5-Table 2)

Operation parameters/chemical properties

Stirring (Equation 9-Table 2)

Solvent evaporated Mass evaporated Boiling point Heat of vaporization Specific heat capacity Time Dryer efficiency Heating Electricity Reactor 1 mass density time Electricity Reactor 2 mass density time Electricity

Filtration (Equation 7-Table 2) Pumping (Equation 8-Table 2)

Waste solvent recovery

Mass filtered Electricity Mass transferred Electricity Solvent recovered Mass of solvent Steam Electricity

Acetone 7022 56 518 2.15 6

kg °C kJ/kg kJ/kg.K h

80% 5132 25 560 977

MJ kWh g kg/m3

10800 210946 5.9E-02 6321.778 798 28800

sec J kWh g kg/m3 sec

413508 0.127636

J kWh

1654 16.5 13867 763

kg kWh kg J

2.12E-04 Acetone 5618 8427 185

kWh kg kg kWh

Lidocaine Intermediates LCI data for all the reagents used in the final synthesis step for lidocaine are not available in existing LCI databases. Similar methods were used to generate LCIs for diethylaniline and diethylamine, based on the synthesis procedures found in the literature.73,74 2,6-dimethylaniline, used in the final steps of synthesis, is produced from a reaction of 2,6dimethylphenol and ammonia. And 2,6-dimethylphenol is obtained from a reaction between phenol and methanol, as shown in rxn 2. While LCI data for ammonia in various forms exist, data for 2,6dimethylaniline and 2,6-dimethylphenol were not available in the Ecoinvent database56 or in other online resources. Hence, process calculations were used to estimate the LCI for 2,6-dimethylaniline, 17 ACS Paragon Plus Environment


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and stoichiometry was used in case of the pre-cursor. The reaction to produce 2,6-dimethylaniline occurs at a temperature of 400 °C in the presence of γ-Al2O3 catalyst.75 Since there was limited data on the production process, only the sensible heat required to raise the temperature to the reaction temperature is considered as the energy input. 2,6-dimethylphenol was another chemical for which the LCI was estimated in the course of generating a LCI for lidocaine. Stoichiometric ratios of the reactants were used to determine the amount of phenol and methanol required for producing 1 kg of 2,6-dimethylphenol, assuming a yield of 70%. The yields of specialty chemicals could be much lower than basic organic chemicals, and a 70% yield is assumed when estimates were not available.7 Table 6 gives the steps in energy calculations for the two chemicals using the process design and stoichiometric calculations. Table 6: Energy calculations using process calculations and stoichiometric LCI methods for 2,6dimethylaniline and 2,6-dimethylphenol LCI Method

Process calculations

Product Unit Process Reactor

LCI Method Product

2,6-dimethylaniline Operation parameters/chemical properties Reactants 2,6-dimethylphenol Ammonia Temperature 400 °C Heat capacity of mixture 2.73 kJ/kg.K Heating 10.5 MJ Stoichiometric calculations 2,6-dimethylphenol

Balanced reaction Molar mass Heat of reaction Energy (𝑄𝑝 )

C6H5OH + 2 CH3OH → (CH3)2C6H3OH + 2 H2O 94.11 64.08 122.16 36.03 -142.12 kJ/mol -101 kJ


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Output 2,6 dimethylphenol Phenol Methanol Cooling water

1 kg 1.10 kg 0.75 kg 101 kJ

Output p-toluenesulfonyl chloride HCl Input Toluene Sulfuryl chloride

Output 2,6 Dimethyl aniline Input 2,6-dimethylphenol Ammonia Heat Output Diethylaniline Input Aniline HCL Ethyl alcohol 30% Caustic soda p-toluenesulfonyl chloride Heat Electricity

1 kg 0.19 kg 0.69 kg 1.01 kg

1 kg

Output Lidocaine Input 2,6-dimethylaniline Chloroacetyl Chloride Potassium Carbonate Diethylamine Acetone Steam Heat Electricity

1.1 kg 0.46 kg 10.5 MJ

1 kg 0.78 kg 0.84 kg 0.66 kg 0.24 kg 3.02 MJ 0.0002 kWh

1.00 kg 1.13 kg 1.58 kg 0.69 kg 0.67 kg 1.40 kg 8.43 kg 5.13 MJ 0.23 kWh

Figure 2: LCI for Lidocaine and its missing intermediates Figure 2 shows LCI for the API along with the other missing LCIs generated which were the part of the synthesis. For all the other inputs seen in the LCIs for lidocaine and its intermediates, the existing LCIs from the ecoinvent database were used.

Results and Discussion The generation of LCI data for drugs used for the provision of anesthesia care was the main objective of this work. However, due to the acute scarcity of the chemical LCIs available in literature and databases, in the process of generating the 20 LCIs for anesthetic drugs, 135 other missing LCIs for intermediates and other chemicals were accounted for. Most of the LCIs, in total 84, were generated by the scale-up procedure, out of which 5 were based only on material use due to lack of information on energy use. Proxies were used for 15 other chemicals. The number of LCIs generated using different methods is shown in Table 7. Table 7: Number of missing LCIs accounted for using different methods Scale-up from lab procedure

Scale-up with Material only

Process Calculations

Stoichiometry

Proxy

Grand Total

79

5

11

45

15

155

Count of LCIs

All 155 LCI datasets, including those of the 20 APIs, can be found in the Supporting Information (SI), along with the synthesis steps, reaction schema, process flow diagrams analogous to those detailed above for lidocaine.



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Life Cycle Impact Assessment (LCIA) The cradle-to-gate GHG emissions for the 20 anesthetic drugs range from 11 kg CO2 eq. per kg succinylcholine to 3,000 kg CO2 eq. per kg dexmedetomidine, and are shown in Figure 3 (Top). The cradle-to-gate GHG emissions for 16 out the 20 drugs assessed is below 200 kg CO2 eq. In comparison with bulk chemicals for which GHG results are in the range of 2-15 kg CO2 eq., the GHG emissions for these complex pharmaceutical compounds is relatively high. This is primarily due to the number of steps required in the synthesis of these APIs from the raw materials extracted from nature. Pharmaceutical compounds typically have lower yields and more synthesis steps compared to the bulk organic chemicals, which results in higher material and energy use, contributing to the higher GHG emissions. For example, the bulk chemical isobutyl acetate has cradle-to-gate GHG emissions of 4 kg CO2 eq. and is produced from crude oil in three steps. In contrast, production of the API lidocaine has cradle-to-gate GHG emissions of 29 kg CO2 eq. and is synthesized in six steps.

Figure 3: (Top) Cradle-to-gate GHG emissions per kg drug for 20 injectable drugs used in anesthesia care, (Bottom) cradle-to-gate GHG emissions per kg API/intermediate from previous studies. Previous results for cradle-to-gate GHG emissions results for APIs and intermediates from these studies are also shown in Figure 3 (Bottom), with study details listed in Table 8. A cradle-to-gate analysis of Cumulative Energy Demand (CED) for the 20 drugs in this study gave results in the range of 115-58,800 MJ/kg API with an average of 5,990 MJ/kg. The only common drug between this study and existing studies is morphine, for which the per kg GHG emission results is significantly lower than in the current study. This is because of the different routes of morphine production explored in the two 20 ACS Paragon Plus Environment

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assessments. McAlister et al.8 in their collaborative work with pharmaceutical companies provide detailed life cycle assessment results for production of morphine sulfate derived from natural opium. In contrast, in this study we present a life cycle inventory and cradle-to-gate GHG emission results for morphine production from a completely synthetic route based on patent literature.76 Since most of global morphine production is from opium, the results from the work McAlister et al.8 are used as the inputs in the hydromorphone LCA instead of the LCI data for morphine through synthetic route. A common feature in these past studies is that the data for the LCI of the APIs in most cases were obtained from the manufacturer. Gate-to-gate LCI generation methodology developed and used by Jiménez-González6 has been further improved in this work by integrating scale-up to factor-in the heat losses in the energy calculations based on the size of the unit operations. Table 8: Cradle-to-gate GHG emissions and Cumulative Energy Demand (CED) for pharmaceutical drugs and intermediates from literature

Authors Jiménez-González, 20006 Wernet et al., 201028 201077

Drug/Intermediate Sertraline Unspecified API (GSK)

GHG emissions

CED

[kg CO2 eq./kg]

[MJ/kg]

2,140 68

1,430

Ponder and Overcash, Poechlauer et al., 201012 Van der Vorst et al., 201178 Ott et al., 201429 Lee et al., 201663

Vancomycin HCl (S)-2,3-dihydro-1Hindole-2-carboxylic acid Galantamine Precursor Z-isomeric compound (Sanofi) 4-D-Erythronolactone

60 125

McAlister et al., 20168

Morphine sulfate

240

4,890 2250 220

In pursuit of simplifying life cycle assessment for complex chemicals through machine learning techniques, thus bypassing the detailed life cycle inventory generation step, various models have been developed to exploit the statistical relationships among various molecular descriptors and thermodynamic properties to predict the impact assessment results.35,36,79 While a detailed analysis of this sort is out of scope of the present work, a simplified assessment of correlation between molecular weight, molecular complexity, and the number of synthesis steps and the cradle-to-gate GHG emissions is presented in Figure 4. Molecular complexity can be calculated in different ways and has important implications for drug discovery and synthesis of pharmaceuticals. 80,81 The values for molecular complexity are obtained online from PubChem which are calculated based on the Bertz’s complexity index which takes into account the elemental diversity of the molecule as well as its structural features such as symmetry.81–83 There is no obvious correlation observed between molecular weight or complexity and GHG emissions which is confirmed by low regression coefficient R2 of 0.04 for both the measures. On the other hand, 21 ACS Paragon Plus Environment


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the R2 value of 0.39 for the correlation between number of synthesis steps and GHG emissions while low, does indicate a trend of high GHG emissions for molecules with greater number of steps in the synthesis.

Figure 4: Correlation between molecular weight, molecular complexity and number of synthesis steps with the cradle-to-gate GHG emission results for 20 APIs

Implications for clinical practice and limitations of the study The lack of LCI data for active pharmaceutical ingredients has been one of the barriers faced by the LCA community when analyzing the environmental sustainability in the health care sector. In the process of generating LCIs for 20 common injectable drugs used for anesthesia care, we also developed LCIs for 130+ other intermediate chemicals and building blocks used primarily in the pharmaceutical industry. The new LCIs for chemicals presented in this work could be used by LCA practitioners in other studies, making necessary changes wherever required. Four different methods based on varying availability of data were used in this work to generate the LCIs. The problem of unavailability of process routes for several APIs was overcome by scaling up synthesis routes found in patent literature. Several authors have previously highlighted the inaccuracies that can results from using bench-scale laboratory results for LCI data generation.34,84,85 Gavankar et al.85 in their LCA study on carbon nanotubes found that the results with data from small-scale operations were overestimated by nearly 100%. In the absence of actual plant data, inventory data for pharmaceutical production must be obtained from patent literature and lab scale experiments, and the present work can serve as a template for scaling up LCI data for pharmaceutical drugs. The results of cradle-to-gate GHG emissions for the 20 drugs indicate the high environmental impact potential of pharmaceutical drugs. The LCIs generated do not account for fugitive emissions during chemical production, which would not impact the GHG results unless the emitted chemical itself is a greenhouse gas, as was demonstrated for several general anesthetics studied previously.10 While the focus of this study was on cradle-to-gate GHG emissions, the cradle-to-gate unit LCI data provided in the SI could be used to examine other categories of environmental impact. Further efforts are 22 ACS Paragon Plus Environment

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required on the use and waste management phases in order to build comprehensive inventories to enable assessment of other impact categories such as ecotoxicity for the life cycle of pharmaceutical drugs. Recent attempts at harmonizing the rules for LCA of pharmaceutical products and processes have been made by proposing draft Product Category Rules (PCR) for the sector.86

Functional considerations The LCI data generated here are provided per unit of mass, following conventions for chemical LCI datasets. Of course, these 20 anesthetic drugs considered have different clinical potencies and specialized uses. Comprehensive anesthesia care uses these drugs in combination, depending on patient and surgical needs. A central tenet of comparative LCA is that options be assessed on the basis of function, and this applies also to LCA for pharmaceuticals, as suggested recently by Emara et al. 87 Analogous guidance from the green chemistry literature advocates for metrics such as F-factor (Function factor), which enables a comparison between two chemical products based on their efficacy per unit or the amount required to provide a function.88 Future research should perform LCAs of clinically equivalent quantities of these drugs, and compare alternative anesthesia care pathways including general, neuraxial (spinals and epidurals) regional (peripheral nerve blocks), local (wound infiltration), sedation, and analgesia care, to better support clinician decision-making where care choices exist.

Supporting Information Synthesis steps, reaction schema, process flow diagrams, synthesis trees, LCI calculations, and detailed results for each pharmaceutical drug in editable MS Excel worksheets.

Acknowledgements This research is based upon work supported by the U.S. National Science Foundation, under CAREER award CBET-1454414. We also acknowledge MaryBeth Rockett, Louis Sokolow, Alba Ilia, and Zhenhao Zheng, participants of Young Scholars Program in 2016 and 2017 at Northeastern University for their contribution in creating the process flow diagrams and reaction schema for the project.



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