CHAPTER 1: Assessing the Sustainability of Syntheses of the Anti-tuberculosis Pharmaceutical Pretomanid by Green Metrics

The sub-area of green metrics in the wider field of green chemistry is now a mature field of study, since the inaugural metric of atom economy was introduced in 1991.¹  Several books^2–6  and reviews^7–14  have been written on the subject, mainly focusing on the work of synthetic organic and process chemists in the pharmaceutical industry. The main purpose of introducing metrics is to provide some kind of measuring tool that can be used to gauge the reaction and synthesis efficiency with respect to input material utilization, waste production, and energy consumption of individual chemical reactions and entire synthesis plans for simple and complex target molecules. The most widely used metric among process chemists in industry that has been termed the “yardstick” by which material efficiencies of scaled-up synthesis plans are judged is process mass intensity (PMI), which is the mass ratio of all input materials to final target product.¹⁵  This metric has now superseded the long-standing overall yield metric, which is the multiplicative product of all reaction yields in a synthesis plan along the longest linear branch. For convenience to the reader, Table 1.1 summarizes a brief listing of traditional metrics and their definitions that have been used by synthetic organic and process chemists to gauge reaction and synthesis performance. Generally speaking, synthesis plans that are traditionally characterized as “efficient” are ones that have the lowest possible number of reaction steps, the highest possible overall yield, a higher proportion of construction steps compared to concession or sacrificial steps, and overall high throughput and reduced process time. When it comes to counting the number of reaction steps in a synthesis plan, process chemists count the number of isolations (i.e., number of purifications) of intermediate products and the final product, rather than counting the total number of chemical transformations involved as is often done for reported academic syntheses. If the number of product isolations is less than the number of chemical transformations, then the synthesis plan contains telescoped or concatenated reactions, meaning that more than one chemical transformation has taken place sequentially in a reaction vessel. Often this is done using a solvent switching technique, where the reaction solvent of the first chemical transformation is evaporated leaving behind the first crude product and a second reaction solvent is added to carry out the following chemical transformation. The second intermediate product is isolated and purified, while the first one is not. Such a strategy clearly reduces solvent demand in work-up and purification phases, particularly recrystallization and chromatographic operations. However, the strategy only works if the by-products and other impurities from the former reaction, such as excess reagents, catalysts, and other additives, do not interfere with the performance of the following one. On the other hand, if the total number of chemical transformations is the same as the number of isolations in a synthesis plan, then this implies that each intermediate product along the way, including the final product, is isolated and purified.

Despite these advances in providing quantitative measures of reaction and synthesis efficiency, the often-discussed concept of sustainability has not yet reached the same level of quantitative rigor. In fact, a recent news report in 2019 revealed the fuzzy nature of the concept and various competing definitions of it by different stakeholders, which adds to the confusion in applying the idea in a scientifically sound manner.¹⁶  The little literature that exists on quantifying what sustainability is has mainly gravitated to thermodynamic issues and energy consumption from fossil-fuel and renewable sources.^17–26  In 2020, we decided to tackle the problem of quantifying sustainability in the context of assessing the degree of sustainability of synthesis plans from the point of view of the provenance of the input materials and energy resources used and the fate of all output materials, including waste materials and the intended final product.²⁷  We reasoned that since scaled-up synthesis plan design represents the core effort made by process chemists and chemical engineers, a practical definition of sustainability would have to be demonstrated according to that activity. We were successfully able to illustrate the application of a quantitative definition of sustainability to the analysis of 22 academic and industrial synthesis plans of vanillin, which is the world's most manufactured flavor ingredient. In that work, we introduced a sustainability index (SI) parameter that could be used along with PMI, sacrificial reagent (SR) consumption, input enthalpic energy (IEE) consumption, and Rowan solvent greenness index (RSGI) to provide an overall picture of efficiency and sustainability for various synthesis plans for a given target molecule. Furthermore, the set of plans could be ranked according to these five attributes using both Borda count^28,29  and poset dominance³⁰  methodologies. These ideas further extend our efforts to formulate a standardized framework for evaluating and reporting synthesis plan greenness, particularly in the process industry.³¹  In this chapter, we apply these quantitative techniques to the analysis of four syntheses of the novel anti-tuberculosis pharmaceutical pretomanid from the common starting material 4-nitroimidazole.^32–35  We chose this compound because its synthesis plans are well documented in the literature and they are sufficiently brief that they can serve the purpose of illustrating our methodologies with minimal difficulty (see Table 1.1 for an overview).

Pretomanid (1, PA-824)¹  is a candidate anti-tuberculosis pharmaceutical^36–40  developed by Pathogenesis Corporation³²  and is currently undergoing Phase III clinical trials under the direction of TB Alliance. The structure of pretomanid possesses a unique [4.3.0] fused bicyclic ring system consisting of a [1,3]-oxazinane ring (ring A) and an imidazole ring (ring B) as shown in Figure 1.1.

The four synthesis plans for this pharmaceutical under consideration in this work are shown in Schemes 1.1–1.4 corresponding to Pathogenesis (Scheme 1.1),³²  Otera (Scheme 1.2),³³  Sorensen (Scheme 1.3),³⁴  and Liu (Scheme 1.4),³⁵  respectively.

In order to maintain a fair comparison among the routes, all plans were evaluated from the same common starting material, namely 4-nitroimidazole, and all metrics were calculated based on a basis production of 1 kg of pretomanid. Scheme 1.5 shows routes to two imidazole intermediates used in the four syntheses originating from 4-nitroimidazole.

The originally discovered Pathogenesis route involves ring opening of an alcohol protected glycidol by 2,4-dinitroimidazole, followed by tetrahydropyran protection of an alcohol group, followed by ring closure and final O-alkylation to a bromobenzyl intermediate. The Otera route was advertised as adopting green chemistry principles, in which the authors stated that their synthesis had a reaction mass efficiency (RME) of 0.138 (or 13.8%) and consumed 258 L of reaction solvents and 46 300 L of total solvents in order to produce 1 kg of PA-824. The RME metric is the reciprocal of the PMI metric and can be expressed as a percentage, since its value is a fraction ranging between 0 and 1. These metric determinations may be directly compared with an RME of 4.1% and a consumption of 171 L of reaction solvents and 79 800 L of total solvents for the Pathogenesis route. The synthetic strategy used to build the molecule is identical to the Pathogenesis route; however, the key green attributes are that the first epoxide ring opening reaction was carried out without reaction solvent (solventless reaction) and that the synthesis was shortened by one step by telescoping two reactions so that the intermediate shown in square brackets in Scheme 1.2 was not isolated. Unlike the linear routes shown in Schemes 1.1 and 1.2, Sorensen and coworkers at Princeton University employed a convergent strategy (see Scheme 1.3) using 2-chloro-4-nitroimidazole instead of 2,4-dinitroimidazole as the starting material. The main selling point of this synthesis is that the starting imidazole did not have the same explosive properties as the dinitro derivative, thus making it a safer reagent to handle. The syntheses of 2-chloro-4-nitroimidazole and 2,4-dinitroimidazole from 4-nitroimidazole are shown in Scheme 1.5. In the Sorensen plan, one branch involves the synthesis of a trichloroethanimidate intermediate labelled as 2. The other branch involves first esterification to form a benzoate derivative, followed by O-alkylation using intermediate 2, followed by N-alkylation with 2-chloro-4-nitroimidazole, followed by tandem ester saponification and ring closure. Finally, Liu and coworkers invented a 7-step linear route from 4-nitroimidazole that followed closely the Pathogenesis strategy with a slight modification in the choice of protecting group in the glycidol starting material from a silyl ether to an n-butyl ester.

The sustainability index (SI)²⁷  for a synthesis plan is defined as the root-mean square of four fractional quantities according to eqn (1.1).

where F_VI, F_VO, F_VP, and F_RE are the mass fraction of valorized inputs, mass fraction of valorized waste outputs, mass fraction of valorized target product, and input enthalpic energy fraction arising from renewable energy sources, respectively. Specifically, these four fractions are given by eqn (1.2)–(1.5).

where M_VI is mass of valorized inputs, M_NVI is mass of non-valorized inputs, W_VO is waste mass of valorized outputs, W_NVO is waste mass of non-valorized outputs, M_product is mass of target product, M$product*$ is mass of target product that is destined to be wasted, (IEE)_renewable is the input enthalpy energy arising from renewable resources, and (IEE)_total is the total input enthalpy energy obtained as a sum of all energy consumption as a result of heating and cooling over all input materials used in a synthesis plan above or below a reference state representing the ambient temperature and pressure conditions of 298 K and 1 atm, respectively. A valorized input material is defined as one arising from renewable or recycled sources such as biomass, scrap metals, or retrieved by-products from other processes. A non-valorized input material is derived from non-renewable sources such as fossil fuels and virgin mineral ores. A valorized output material is defined as one destined to be recycled, reclaimed, or used in other processes. A non-valorized output material is defined as one that will end up as “dead waste” whether or not it undergoes treatment before release into the four main environmental compartments of air, water, soil, and sediment. The following energy sources are considered as renewable: hydroelectric, solar, wind, geothermal, and biofuels; and the following energy sources are considered as non-renewable: coal, other fossil-fuels such as petroleum and natural gas, and nuclear. According to the above mass quantities, the process mass intensity can be expressed as shown in eqn (1.6).

Based on this formalism, a given synthesis plan can therefore be said to be completely “sustainable” if the following conditions are satisfied: F_VI = 1, F_VO = 1, F_VP = 1, F_RE = 1, and SI = 1. Conversely, a given synthesis plan can be said to be completely “unsustainable” if the following conditions are satisfied: F_VI = 0, F_VO = 0, F_VP = 0, F_RE = 0, and SI = 0. Since each of the contributing fractions ranges between 0 and 1, then SI is also a fraction that can be expressed as a percentage. In the determination of SI, a number of limiting assumptions need to be made. If ethanol was used as an input material, then 10% of it was assumed to originate from renewable sources (i.e., biomass) if the publication is dated after 1990, since that is the approximate time frame when biofuels were made widely available in the market. Water was considered a renewable input material due to the circulating global hydrological cycle. Mineral salts, metal-derived catalysts, and all non-aqueous and non-biologically derived materials from fossil fuels or ores were considered non-renewable inputs since their rate of renewal occurs on geological time scales that are several orders of magnitude longer than organism time scales. An arbitrary value of 0.9 was used for F_VP indicating that 90% of the manufactured pretomanid pharmaceutical is used as intended and 10% of it is wasted either by excretion from the human body or by shelf degradation in pharmacies. If a synthesis plan was published on or after the year 2000, F_RE = 0.35 following recent energy mix data,^43,44  and if it was published before 2000 then F_RE = 0. The cut-off year 2000 was chosen since it marked the beginning of the 21st century when ideas of sustainability began to take root in the general societal consciousness. In terms of degree of sustainability according to the definition of SI given in eqn (1.1), the Otera, Sorensen, and Liu plans are all tied at SI = 0.4823 and the Pathogenesis plan has SI = 0.4500. Table 1.2 summarizes the fractional breakdown of the contributing factors to SI for each plan. Overall, there is little differentiation between the plans based on the two fractions F_VP = 0.9 (all plans), and F_RE = 0.35 (Liu, Otera, Sorensen) or F_RE = 0 (Pathogenesis) since these are set by the assumptions made. Generally, in SI analyses the greatest variation among synthesis plans arises from the F_VI and F_VO fractions. As observed in Table 1.2, the magnitudes of F_VP and F_RE significantly outweigh those of F_VI and F_VO and so their contribution to the magnitude of SI is larger. However, if reaction, work-up, and purification solvents are retrieved for re-use in the same syntheses or for entirely different syntheses, the value of F_VO (mass fraction of valorized outputs) dramatically increases for all plans according to the following: 0.0097 to 0.988 (Liu), 0.0010 to 0.989 (Otera), 0.0089 to 0.987 (Pathogenesis), and 0.0047 to 0.981 (Sorensen). This observation is not surprising since solvent consumption in all phases of carrying out reaction steps constitutes the bulk of materials used. In turn, the increase in F_VO has the effect of increasing the corresponding value of SI for each plan as shown in Table 1.3 where a 43 to 48% increase in value is calculated. The ranking of the four plans is also more spread out where the ranking order is Otera ∼ Liu > Sorensen > Pathogenesis.

Table 1.4 summarizes the results of the metrics analysis based on the five attribute parameters PMI, SR, IEE, RSGI, and SI for each of the four pretomanid plans considered based on a production of 1 kg of the pharmaceutical from 4-nitroimidazole. PMI was determined according to eqn (1.6). The Liu plan has the lowest PMI value of 16 tonnes per kg and the Sorensen plan has the highest value of 80 tonnes per kg. In both cases, 97.5% of the PMI value arises from purification solvent consumption. The sacrificial reagent (SR) consumption parameter tracks the mass fraction of sacrificial reagents used in a synthesis plan compared to the total mass of reagents used, where sacrificial reagents are defined as those that do not contribute any atoms to the final product structure. Hence, SR is more probing than atom economy since it is directly linked with the final target bond map of the final product structure in a synthesis plan, which traces the origin of each atom back to the contributing reagent atoms and which target bonds were made in which reaction steps. Any reagents not included in this mapping are automatically classified as sacrificial. Eqn (1.7) shows the mathematical definition of SR.

Typically, sacrificial reagents are used in protecting and de-protecting group reaction steps, and oxidation and reduction reaction steps that do not contribute oxygen atoms or hydrogen atoms, respectively, toward the final target structure. Clearly, one key feature of a well-designed synthesis plan is that it maximizes its reagents consumption toward the building up of the target molecule, so that each reaction step is a target bond forming reaction. In the Pathogenesis plan (Scheme 1.1), the following sacrificial reagents were used: nitric acid (for the synthesis of 2,4-dinitro-1H-imidazole, see Scheme 1.5), 3,4-dihydro-2H-pyran (step 4), tetra-n-butylammonium fluoride (step 5), water (step 6), and sodium hydride (step 7). In the Otera plan (Scheme 1.2), the following sacrificial reagents were used: nitric acid (step 1), cinnamic acid and dicyclohexyldiimide (DCC) (step 4), tetra-n-butylammonium fluoride and methanol (step 5), and sodium hydride (step 6). In the Sorensen plan (Scheme 1.3), the following sacrificial reagents were used: bromine and sodium bicarbonate (step 1), methylal (step 2), sodium sulfite and water (step 3), p-methoxybenzoyl chloride and trichloroacetonitrile (step 3*), hydrochloric acid and water (step 4), potassium carbonate (step 5), and potassium hydroxide (step 6). In the Liu plan (Scheme 1.4), the following sacrificial reagents were used: dihydropyran (step 4), methanol and potassium carbonate (step 5), methanol (step 6), and sodium hydride (step 7). Among these four plans, the Otera plan utilizes the least sacrificial reagents at 5 kg per kg pretomanid and the Sorensen plan utilizes the most at 101 kg per kg pretomanid.

Based on the temperature and pressure reaction conditions for each reaction step, the input enthalpy energy (IEE) parameter tracks the enthalpic energy requirements from heating or cooling operations in the reaction, work-up, and purification phases. The largest contributor to IEE arises from heating or cooling reaction solvents since solvents constitute the bulk of the input materials used in a synthesis plan. Among the four plans, the Pathogenesis plan utilizes the least input energy at 5700 kJ per kg pretomanid and the Sorensen plan utilizes the most at 24 000 kJ per kg pretomanid. In the Pathogenesis plan, steps 1 and 7 were carried out under cooling conditions at 0 °C and −60 °C, respectively, whereas, steps 2, 3, and 6 were carried out under heating conditions at 115 °C, 70 °C, and 45 °C, respectively. By contrast, in the Sorensen plan, steps 1, 3, 3*, 4*, and 6 were carried out under cooling conditions at 5 °C, 12 °C, 0 °C, 0 °C, and 0 °C, respectively, whereas, steps 1, 2, 4, and 5 were carried out under heating conditions at 65 °C, 40 °C, 95 °C, and 120 °C, respectively.

The Rowan solvent greenness index (RSGI)⁴⁵  quantifies the relative environmental, toxicological, and safety-hazard impacts of solvents used in reaction, work-up, and purification procedures for all reaction steps in a synthesis plan. It utilizes an overall solvent index (OSI) defined in eqn (1.8) that scales between 0 and 12 spanning the benign solvent water to the non-benign solvent benzene.

where m_i is the mass of solvent i and OSI₁₂ is defined as a normalized quantity over a set of solvents as shown in eqn (1.9).

where OSI_min and OSI_max are the minimum and maximum values of OSI for a set of solvents and OSI_i for a given solvent i is given by eqn (1.10).

where the metric parameters (M) cover occupational exposure limit (OEL, ppm), LD50 (ingestion toxicity, mg kg⁻¹), LC50 (inhalation toxicity, g m⁻³ for 4 h), global warming potential (GWP, unitless), smog formation potential (SFP, unitless), ozone depletion potential (ODP, unitless), acidity-basicity potential (ABP, unitless), bioconcentration potential (BCP, unitless), persistence potential (PER, unitless), soil sorption coefficient (soil, K_oc), half-life of the solvent in the environment (half-life, h), aquatic toxicity to fish (aqua, mg L⁻¹ for 96 h), Q-phrase potential (Q-phrase, unitless), skin dose (SD, mg), and flash point (FP, degrees K). From eqn (1.8), it is observed that high values of RSGI can arise from high mass utilization of solvents, particularly in chromatographic purification steps (i.e., high m values), as well as high impact solvents (i.e., high OSI12 values). Synthesis plans that minimize solvent usage across the board and those that use benign solvents will have low overall RSGI values. Based on the RSGI metric, the Liu plan had the least solvent impact at 105 tonnes and the Sorensen plan had the most at 555 tonnes. The most impactful solvents used in the Liu plan were chlorobenzene, acetic anhydride, dichloromethane, and dimethylformamide. In the Sorensen plan, they were hexane, dichloromethane, dimethylformamide, and methyl t-butyl ether. The high RSGI value in the Sorensen plan arises mainly from the large solvent consumption in the chromatographic purification operations in steps 3*, 4*, 5, and 6.

In order to implement an unbiased ranking of synthesis plans according to various metrics, there are two well-documented methods for doing this, namely, the Borda count^28,29  method and the poset dominance³⁰  method. The Borda method is easy to implement and is also rapid in carrying out the computation. The poset dominance method involves a more tedious calculation but yields a more reliable result, since it considers all possible pairwise comparisons of attributes across all pairwise comparisons of synthesis plans. In the Borda count method, the plans are listed in ascending order of PMI, SR, IEE, and RSGI so that the plans having the lowest values for these attributes are ranked highest, and the plans are listed in descending order of SI so that the plans having the highest values are ranked highest. The highest score corresponds to the number of plans considered. In this case, since there are four pretomanid plans under consideration, the Borda scoring will have values of 1, 2, 3, or 4. Once these points are assigned for each attribute, the scores are tallied up and an overall Borda count is obtained for each plan. The plans are then ranked accordingly in descending order to obtain a final ranking order. Table 1.5 shows a summary of the Borda count rankings for the four pretomanid plans according to: Liu > Otera > Pathogenesis ≫ Sorensen. The Liu plan scored highest in three attributes: PMI, RSGI, and SI; whereas the Otera plan scored highest in two attributes: SR and SI. The Pathogenesis plan scored highest in only the IEE attribute, and the Sorensen plan ranked lowest in all attributes except SI.

In the poset dominance method, we first need to determine the number of pairwise attributes and the number of pairwise plan comparisons for each pairwise attribute in order to determine the overall size of the ranking exercise. Since there are five attributes, there are 5!/((5 – 2)! 2!) = 10 pairwise attribute comparisons. The explicit list is as follows: PMI versus SR, PMI versus IEE, PMI versus RSGI, PMI versus SI, SR versus IEE, SR versus RSGI, SR versus SI, IEE versus RSGI, IEE versus SI, and RSGI versus SI. Since four synthesis plans are considered, there are 4!/((4 – 2)! 2!) = 6 pairwise plan comparisons that need to be made. Therefore, in total there are 10 × 6 = 60 pairwise comparisons that need to be made in the entire poset analysis for this illustrative example of pretomanid plans. For a given pairwise plan comparison, for a pair of attributes there are two possible outcomes: (a) a comparable pair in which plan A dominates plan B for both attributes X and Y; and (b) an incomparable pair in which plan A dominates plan B for attribute X and plan B dominates plan A for attribute Y. When a comparable pair for a given pairwise attribute comparison is found the dominant plan is identified. This sequence of steps is repeated for each pairwise attribute comparison and then the number of dominant occurrences for each plan is tallied up. As an example, if we consider the PMI versus SR comparison, we find that the ranking order for PMI is Liu > Otera > Pathogenesis > Sorensen and the ranking order for SR is Otera > Pathogenesis > Liu > Sorensen. The Liu versus Otera and Liu versus Pathogenesis comparisons result in incomparable pairs for both the PMI and SR attributes, i.e., Liu dominates Otera for PMI but Otera dominates Liu for SR, and Liu dominates Pathogenesis for PMI but Pathogenesis dominates Liu for SR. However, the Liu versus Sorensen comparison results in a comparable pair since the Liu plan dominates the Sorensen plan in both PMI and SR attributes. Furthermore, the Otera plan dominates the Pathogenesis and Sorensen plans in both PMI and SR, and the Pathogenesis plan dominates the Sorensen plan in both PMI and SR. As a result of these pairwise comparisons, the Liu plan is assigned 1 dominance, the Otera plan is assigned 2 dominances, the Pathogenesis plan is assigned 1 dominance, and the Sorensen plan is assigned a 0 dominance. Table 1.6 summarizes the results of the 60-pair poset dominance analysis for the four pretomanid plans where the overall ranking order is as follows: Otera = Liu > Pathogenesis ≫ Sorensen. We observe that both ranking methods essentially give the same ranking result, since there is only a one-point difference between the Otera and Liu methods in the Borda count method compared to identical points in the poset method.

In this chapter, we have illustrated how process mass intensity (PMI), sacrificial reagent (SR) consumption, input enthalpic energy (IEE) consumption, Rowan solvent greenness index (RSGI), and sustainability index (SI) based on valorized input and output materials and fraction of renewable energy consumption can be used to evaluate overall synthesis plan greenness. Based on these five key attributes, it is possible to rank synthesis plans in an unbiased way by using Borda count or poset dominance analysis. In this illustrative example of four pretomanid syntheses beginning from the same starting material, we find that the Otera plan's claim to follow green chemistry principles is supported by the present quantitative analysis. We also have shown that the competing plan documented by Liu and coworkers is also highly ranked. Further improvements to the synthesis of this pharmaceutical are always possible and such plans can be evaluated by the same methodology described in this work, provided that full disclosure of their plan details is made. The sustainability index determined from input provenance and output fate is sensitive to various assumptions in the determination of the four contributing fractions and hence its reliability is strongly governed by the availability of detailed experimental procedures, and thermodynamic (temperature dependent heat capacity functions for substances and equation of state data), toxicological, and safety-hazard parameters for all materials involved. The most challenging contributing mass fraction to SI to determine is FVP, since there are no repository databases that keep track of each chemical commodity's fate once it is produced by any sector of the chemical industry. Nevertheless, the evaluation of SI is straight forward, and it is hoped that it will find use among process chemists to evaluate their plans according to green chemistry principles in a more rigorous, robust, and convincing way.