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Evolving green chemistry metrics into predictive tools for decision making and benchmarking analytics Jun Li, Jacob Albrecht, Alina Borovika, and Martin D. Eastgate ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b03407 • Publication Date (Web): 01 Nov 2017 Downloaded from http://pubs.acs.org on November 2, 2017
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ACS Sustainable Chemistry & Engineering
Evolving Green Chemistry Metrics into Predictive Tools for Decision Making and Benchmarking Analytics Jun Li, Jacob Albrecht, Alina Borovika and Martin D. Eastgate* Chemical and Synthetic Development, Bristol-Myers Squibb, 1 Squibb Drive, New Brunswick, NJ, 08903 (USA) Fax: (+1-732-227-5148) E-mail:
[email protected] ABSTRACT: Designing efficient and green approaches to complex molecules is a challenge faced by any organization seeking to deliver modern pharmaceutical compounds to patients. The outcome of any route design effort, in terms of efficiency, is largely governed by the disconnections and synthetic strategies generated during the process of route scouting, coupled with the decisions made by the individuals responsible for the research. In this article, we delineate an approach, based on historical data, capable of quantifying the probable efficiency of a proposed synthesis prior to any research being conducted. This decision making strategy can be used to both aid the decision making process of innovators, and to benchmark the outcome performance of the developed process, improving the efficiency of any manufacturing process developed. Through improved decision making and benchmarking, this approach could minimize the environmental impact of pharmaceutical production.
Keywords: PMI, Predictive Analytics, Option Analysis, Synthetic efficiency, Green Chemistry
Introduction The importance of green and sustainable manufacturing cannot be understated. It is one of the major challenges facing all chemical industries in the modern era. For those in the process development organizations of the pharmaceutical industry, the rapidly increasing complexity of modern pharmaceuticals1 poses a significant challenge to the development of efficient and green manufacturing processes. Molecules such as Halaven™ (Eribulin, developed by Eisai),2,3,4,5 which contains 19 stereogenic centers, 9 ring systems and is prepared commercially by total synthesis, raise important questions for those seeking to implement and contextualize the principles of green chemistry and process
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efficiency. Questions such as, what does a green synthesis for a molecule as complex as Halaven look like? How do we define it? And, how do we know when an efficient route has been achieved? Common
metrics,
such
∑ ( . 6,7,8,9 ), ( )
as
process
mass
intensity
(PMI=
provide little context for a molecule such as Halaven. Indeed
the PMI of such a molecule would be irrelevant in absolute terms – it is obviously going to be a large number incomparable to a traditional small molecule. In order to make any judgement on efficiency, we need to be able to assess metrics, such as PMI, in a broader context. While there are many definitions of ‘green chemistry’ it is still relatively hard to define despite several authors laying out compelling discussions on the topic.10 Principles such as the ‘12 principles of green chemistry’,11,12 have become part of our lexicon of efficiency, and have been expanded,13 helping to frame discussions of what it takes to produce an efficient (green) manufacturing process. However, many of these concepts are not knowable during key periods of decision making, or are focused on optimization post route identification. Route selection [the decision of which route of synthesis should be moved forward to regulatory approval and commercial supply] is one of the key decisions of a pharmaceutical development organization, a defining moment that determines the manufacturing route of the active pharmaceutical ingredient (or API). There are several factors that are typically considered during this route selection process, such as safety, efficiency, cost, quality and robustness. In the modern era, the development of a de novo synthesis to a drug candidate is often critical to supplying the therapeutic commercially.14 While innovation can help tame the complexity of molecules, the process of innovation in synthesis is essentially a problem in decision making. Out of the multitude of synthetic options, only a very few can be pursued in the laboratory, and out of any potential early routes identified only a single approach can be taken forward to validation and commercialization (where the bulk of process intensification and green chemistry considerations are applied). Thus the early decisions of which synthetic options to pursue, and which demonstrated approaches should be selected for development, are made largely without data regarding the routes future potential efficiency (i.e. the potential optimized efficiency of the route) – thus this critical selection process is done largely in the absence of quantitative efficiency data and therefore is governed by individual bias and assumption. A situation prone to error, inaccuracy and unlikely to reliably maximize efficiency. There are several additional complicating factors. For example, as molecular systems become more complex, the reactivity of organic molecules becomes more and more unpredictable and properties such as solubility or reactivity, or key decisions on reagent selection, cannot be easily assessed a priori. The combination of increasing synthetic options, the lack of knowledge regarding viable solvents,
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reagents or conditions (the main drivers of PMI) complicate the implementation of green chemistry principles during the initial design of a new synthesis –limiting the attainment of optimized efficiency. This poses a question – how can one optimize the selection of a specific route during route scouting without any concrete data on its potential end-state (post optimization) efficiency? If an organization wishes to optimize the greenness of the synthetic approaches it develops, this is a foundational question. However, it is in this context that probable efficiency discussions should begin, at the inception of route design. It is our assertion that synthetic strategy is a foundational principle of green chemistry. One approach to solving this conundrum is to focus on predicting the holistic impact of a synthesis, rather than focusing on individual considerations at the point of route innovation. For example, while it is certainly true that lower step count may lead to greater efficiency, certain reaction types are highly inefficient when compared to other processes; for example, catalytic transformations may be highly efficient, others may not represent the most efficient way to prepare a given molecule. As we previously revealed, PMIs for reactions can vary significantly with some unexpected transformations having low efficiencies (such as homogeneous metal catalyzed processes, which generally have a high probability of having a relatively poor PMI).15 This is counter-intuitive as catalytic processes are often considered to be inherently green – however, it is highly dependent on the specific chemistry involved and needs to be contextualized with the other options available (ie what the alternate routes are). Thus, the critical piece of information innovators need at the time of invention is data concerning the probability of route proposals having improved efficiencies vs any other given approach, ie which route proposal has the highest potential to be green and by what magnitude. Faced with the dynamic landscape of early drug discovery, the time critical supply of clinical trial materials, the stringent nature of developing regulated science and validation-ready processes, through to the rigors of commercial supply, process chemistry is a critical enabler meeting various requirements within the pharmaceutical supply chain.16 With the high rate of attrition in drug development,17 increasingly limited R&D resources and increasingly tight timelines,18,19 we believe it has become essential to establish new decision-making frameworks to help navigate the stochastic drug development processes. From a green chemistry perspective, the combination of limited R&D resources, short timelines for drug development and reduced time on market, mean that the decisions innovators make in route development have an exacerbated impact on end-state efficiency – where companies are less likely to revisit commercial manufacturing approaches post filing, nor develop multiple competing processes during development, to select the most efficient option. This manuscript describes an approach to evaluate potential route efficiency prior to experimental exploration. Our method is based on predicting PMI for a proposed route (using historical real world
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data), enabling an estimation of the probable efficiency of the strategies being considered. We envision that this simulation approach can be developed to evaluate potential synthetic routes prior to their exploration and development, to drive the routine selection of the highest potential efficiency synthesis. Background and Approach Green chemistry metrics serve to quantify the efficiency and environmental performance of chemical processes.20,21,22 Since the introduction of the first metrics, this has been an active area of research and discussion, with new metrics, such as Green Aspiration Level (GAL),23,24 being recently proposed. These approaches enable researchers to assess the impact of their work, at least across certain parameters, though usually in hindsight. Historically, Process Mass Intensity (PMI), introduced by the Green Chemistry Institute Pharmaceutical Roundtable as an extension of E factor,21 has been the most widely adopted within the chemical and pharmaceutical industries.25 Many companies have developed internal approaches to incorporate both mass intensity metrics, along with other aspects of process greenness (such as those delineated in the twelve principles of green chemistry)11 in their decision making processes. Within BMS we developed an approach based around a ‘process greenness scorecard’ which evolved over time into a web-based tool for our scientists to systematically assess and track both environmental parameters and safety aspects of our manufacturing processes, across phases of development, scales and research programs.26 A key piece of data included in our scorecard was scale-up PMI data, collated across molecules of different size, complexity, efficiency (at the step level), scales and phases of development (IND-tox through to validation). As we had this large volume of data, captured in a systematic way and within a single database, we sought to leverage this ‘real world’ information in a predictive sense, with the goal of developing an approach to predicting the efficiency of undeveloped route proposals. Initially, we hypothesized that each reaction type may exhibit a characteristic distribution of PMI, reflective of the operational complexity of the transformation and not specifically linked to the molecule itself. If properly aggregated, this data could effectively summate the efficiency of a range of chemical reactions, on industrial scale, in a wide range of settings.15 Analysis of our database quickly revealed that this hypothesis was correct. A simple classification of reaction types (such as chlorination, amide formation, Suzuki coupling etc) enabled us to segment the efficiency data to reveal that reactions have characteristic PMI signatures, presumably reflective of the operational complexity of the transformation. For example, reactions such as metal mediated couplings tended to have a higher PMI distributions, owing to generally lower yields and more complicated work-up
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and isolation procedures. Simpler transformations, such as halogenation or acylation, had a relatively improved PMI due to their higher yields and simple isolation. With this data in hand, we proceeded to link step PMI and step yield into a simple algorithm to replicate a full synthesis computationally, and were able to use a Monte Carlo simulation to aggregate the observed PMI ranges, with an estimation of potential yield distributions, into a prediction of the potential PMI distribution.15 The output of this predictive analysis is a PMI range, reflective of the probability of obtaining a PMI within that range, upon optimization. In our initial study, we performed an analysis of historical projects within the BMS portfolio to demonstrate the accuracy of these predictions against the actualized outcomes. In essence, the segmentation of efficiency parameters into discrete reaction types (i.e. generic ‘chlorination’ or ‘amide formation’), with defined PMI ranges enabled the simulation of any sequence of synthetic transformations proposed. The utilization of real-world scale-up data produced a prediction of the probable efficiency of the optimized sequence on-scale. We hoped that this approach would facilitate “green-by-design” 27 route innovation, i.e. improve decisions at the point of genesis of a synthesis. The use of real-world data results in the predictions reflecting the sum of all prior similar chemistries. Thus, this approach can also serve as a benchmarking exercise, assessing how the development of the realized synthesis compares against prior art. Comparing a PMI outcome to all similar chemistry contextualizes the efficiency for the process, delineating efficiency vs other similar work, finally allowing us to address the question of how efficient a synthesis of a molecule is against its peers. However, there are several known liabilities associated with the use of PMI, which may preclude its use in prediction: a. The lack of alignment on the starting points of PMI calculations. b. PMI does not take into account the synthesis of reagents that are typically depicted above and below the arrow of a reaction scheme. While the first point does not significantly impact our methodology per se, it points to an issue of comparison. If we are to use PMI prediction as a method of selection, or of comparison between routes, we need to be consistent in our selection of the starting point for the analysis. Roschangar et al, in their disclosure of the Greenness Aspiration Level (GAL),23 proposed a standardization for a starting point, defining starting materials as those costing 1 for reagents charged in excess) the molecular weights ( , ) and yield for each input , at each step that outputs material : , = "# → ,
(1)
where the reference factor28 , "#, is defined for each input at each step as: "# → =
%,& '(%
(2)
) & '( &
In addition, each processing step requires non-substrate inputs, such as filter media, solvents, catalysts, etc. that must be disposed. Expressing this as a mass-basis ratio, *, the total non-substrate mass needed at step is: + ,
, = * ∙ ,
(3)
Through a synthetic sequence, the total amount of mass required is: = ∑ * ∙ + ∑ /
(4)
The total mass per step , the sum of the substrate and non-substrate inputs, is used to determine the step PMI to a compound: *01234 =
∑% %,& 5 67689:;9
