# The application of design of experiments (DoE) reaction optimisation and solvent selection in the development of new synthetic chemistry

Paul Murray, Fiona Bellany, Laure Benhamou, Dejan-Krešimir Bučar, Alethea Tabor, Tom Sheppard

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Paul Murray, Fiona Bellany, Laure Benhamou, Dejan-Krešimir Bučar, Alethea Tabor, et al.. The application of design of experiments (DoE) reaction optimisation and solvent selection in the development of new synthetic chemistry. Organic & Biomolecular Chemistry, 2016, 14 (8), pp.2373-2384. 10.1039/C5OB01892G. hal-04344330

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Organic &
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Paul M. Murray, Tom D. Sheppard et al.

The application of design of experiments (DoE) reaction optimisation and
solvent selection in the development of new synthetic chemistry

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Organic &
Biomolecular
Chemistry

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ISSN 1477-0520

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ROYAL SOCIETY
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REVIEW ARTICLE

Paul M. Murray, Tom D. Sheppard et al.

The application of design of experiments (DoE) reaction optimisation and
solvent selection in the development of new synthetic chemistry

175
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Cite this: *Org. Biomol. Chem.*, 2016, **14**, 2373

# The application of design of experiments (DoE) reaction optimisation and solvent selection in the development of new synthetic chemistry†

Paul M. Murray,*a Fiona Bellany,b Laure Benhamou,b Dejan-Krešimir Bučar,b Alethea B. Taborb and Tom D. Sheppard*b

This article outlines the benefits of using 'Design of Experiments' (DoE) optimisation during the development of new synthetic methodology. A particularly important factor in the development of new chemical reactions is the choice of solvent which can often drastically alter the efficiency and selectivity of a process. Whilst solvent optimisation is usually done in a non-systematic way based upon a chemist's intuition and previous laboratory experience, we illustrate how optimisation of the solvent for a reaction can be carried out by using a 'map of solvent space' in a DoE optimisation. A new solvent map has been developed specifically for optimisation of new chemical reactions using principle component analysis (PCA) incorporating 136 solvents with a wide range of properties. The new solvent map has been used to identify safer alternatives to toxic/hazardous solvents, and also in the optimisation of an *S*<sub>N</sub>Ar reaction.

Received 11th September 2015
Accepted 15th December 2015

DOI: 10.1039/c5ob01892g

www.rsc.org/obc

## Introduction – why use DOE?

The development of new synthetic methodology is a key part of academic chemistry research, focusing both on the discovery of intrinsically novel reactions, as well as the identification of improved methods for carrying out existing transformations. Developments made in this area can ultimately determine which molecules are deemed to be ‘accessible enough’ to be suitable for a wide range of practical applications, including pharmaceuticals, agrochemicals, polymers and other functional materials, flavours/fragrances, biological probes, etc. The importance of new synthetic methodology has been recognised by the award of several Nobel prizes in recent years for the development of synthetically important reactions (asymmetric catalysis; metathesis; cross-coupling). These reactions have not only been used extremely widely by chemists in both academia and industry, but have even played a significant role in dictating which molecules are employed in many of the above applications.

The uptake of novel synthetic methodology by researchers in industry and in other scientific fields is much more likely if the chemistry can be demonstrated to be ‘user friendly’. Important factors which can facilitate uptake of a particular reaction include: readily available reagents/catalysts; a wide substrate scope; good functional group compatibility; mild conditions; efficiency; sustainability and a good safety profile. However, such factors are rarely taken into account during the development of new chemistry. As noted by industrial researchers,¹ many synthetic methodology papers fail to adequately explore the substrate scope of a new reaction and instead focus on reactions of largely unfunctionalised lipophilic compounds. Furthermore, despite the fact that well established statistical methods for reaction optimisation are widely used in industry,²,³ the uptake of these methods has been very low in academic chemistry.⁴,⁵ Often, the ‘optimisation’ process proceeds entirely *via* a trial and error approach involving the variation of one factor at a time (*e.g.* solvent, temperature, catalyst, concentration, *etc*.). This type of process can lead to researchers failing to identify ‘optimal’ conditions for a particular process if interactions between two or more factors are present.⁶ Thus, an attempt to optimise even two factors *via* a ‘one variable at a time’ (OVAT) approach can fail to find the optimum conditions if interactions between the factors are present (Fig. 1). For example, initial optimisation of an imaginary reaction *via* variation of the number of equivalents of reagent and the temperature involves variation of the first variable whilst keeping *T* = 40. This suggests that 2 equivalents of reagent give the ‘best yield’. Subsequent variation of the

<footnote><sup>a</sup>Paul Murray Catalysis Consulting Ltd, 67 Hudson Close, Yate, BS37 4NP, UK.
E-mail: paul.murray@catalysisconsulting.co.uk</footnote>

<footnote><sup>b</sup>Department of Chemistry, University College London, 20 Gordon St, London, WC1H 0AJ, UK. E-mail: tom.sheppard@ucl.ac.uk; Tel: +44 (0)20 7679 2467</footnote>

<footnote><sup>†</sup>Electronic supplementary information (ESI) available: Principle component values for the new PCA solvent map, experimental procedures, spectroscopic data and <sup>1</sup>H and <sup>13</sup>C NMR spectra. CCDC 1423524 and 1423525. For ESI and crystallographic data in CIF or other electronic format see DOI: 10.1039/C5OB01892G</footnote>
<figure><img src="image_4.png" /></figure>

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Cite this: *Org. Biomol. Chem.*, 2016, **14**, 2373

# The application of design of experiments (DoE) reaction optimisation and solvent selection in the development of new synthetic chemistry†

Paul M. Murray,*a Fiona Bellany,b Laure Benhamou,b Dejan-Krešimir Bučar,b Alethea B. Taborb and Tom D. Sheppard*b

This article outlines the benefits of using 'Design of Experiments' (DoE) optimisation during the development of new synthetic methodology. A particularly important factor in the development of new chemical reactions is the choice of solvent which can often drastically alter the efficiency and selectivity of a process. Whilst solvent optimisation is usually done in a non-systematic way based upon a chemist's intuition and previous laboratory experience, we illustrate how optimisation of the solvent for a reaction can be carried out by using a 'map of solvent space' in a DoE optimisation. A new solvent map has been developed specifically for optimisation of new chemical reactions using principle component analysis (PCA) incorporating 136 solvents with a wide range of properties. The new solvent map has been used to identify safer alternatives to toxic/hazardous solvents, and also in the optimisation of an S<sub>N</sub>Ar reaction.

Received 11th September 2015
Accepted 15th December 2015

DOI: 10.1039/c5ob01892g

www.rsc.org/obc

## Introduction – why use DOE?

The development of new synthetic methodology is a key part of academic chemistry research, focusing both on the discovery of intrinsically novel reactions, as well as the identification of improved methods for carrying out existing transformations. Developments made in this area can ultimately determine which molecules are deemed to be ‘accessible enough’ to be suitable for a wide range of practical applications, including pharmaceuticals, agrochemicals, polymers and other functional materials, flavours/fragrances, biological probes, etc. The importance of new synthetic methodology has been recognised by the award of several Nobel prizes in recent years for the development of synthetically important reactions (asymmetric catalysis; metathesis; cross-coupling). These reactions have not only been used extremely widely by chemists in both academia and industry, but have even played a significant role in dictating which molecules are employed in many of the above applications.

The uptake of novel synthetic methodology by researchers in industry and in other scientific fields is much more likely if the chemistry can be demonstrated to be ‘user friendly’. Important factors which can facilitate uptake of a particular reaction include: readily available reagents/catalysts; a wide substrate scope; good functional group compatibility; mild conditions; efficiency; sustainability and a good safety profile. However, such factors are rarely taken into account during the development of new chemistry. As noted by industrial researchers,¹ many synthetic methodology papers fail to adequately explore the substrate scope of a new reaction and instead focus on reactions of largely unfunctionalised lipophilic compounds. Furthermore, despite the fact that well established statistical methods for reaction optimisation are widely used in industry,²,³ the uptake of these methods has been very low in academic chemistry.⁴,⁵ Often, the ‘optimisation’ process proceeds entirely *via* a trial and error approach involving the variation of one factor at a time (*e.g.* solvent, temperature, catalyst, concentration, *etc*.). This type of process can lead to researchers failing to identify ‘optimal’ conditions for a particular process if interactions between two or more factors are present.⁶ Thus, an attempt to optimise even two factors *via* a ‘one variable at a time’ (OVAT) approach can fail to find the optimum conditions if interactions between the factors are present (Fig. 1). For example, initial optimisation of an imaginary reaction *via* variation of the number of equivalents of reagent and the temperature involves variation of the first variable whilst keeping *T* = 40. This suggests that 2 equivalents of reagent give the ‘best yield’. Subsequent variation of the

<figure><img src="image_5.png" /></figure>

<footnote><sup>a</sup>Paul Murray Catalysis Consulting Ltd, 67 Hudson Close, Yate, BS37 4NP, UK.
E-mail: paul.murray@catalysisconsulting.co.uk</footnote>

<footnote><sup>b</sup>Department of Chemistry, University College London, 20 Gordon St, London, WC1H 0AJ, UK. E-mail: tom.sheppard@ucl.ac.uk; Tel: +44 (0)20 7679 2467</footnote>

<footnote>†Electronic supplementary information (ESI) available: Principle component values for the new PCA solvent map, experimental procedures, spectroscopic data and ¹H and ¹³C NMR spectra. CCDC 1423524 and 1423525. For ESI and crystallographic data in CIF or other electronic format see DOI: 10.1039/C5OB01892G</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 1 The pitfalls of traditional ‘one variable at a time’ (OVAT) optimisation.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 2 A DoE study covering the entire reaction space will not miss the optimum conditions provided it lies within the space covered.</figcaption></figure>

temperature whilst keeping eq. = 2 suggests that the optimum conditions are T = 55, eq. = 2. However, due to interaction between the factors this fails to identify the true optimum conditions where a higher yield of product can be obtained using smaller quantities of reagent (T = 105, eq. = 1.25). This is a consequence of the fact that the full reaction space has not been explored and at no-point was the combination of high T/low eq. considered.

The technique of ‘Design of Experiments’ is a statistical approach to reaction optimisation that allows the variation of multiple factors simultaneously in order to screen ‘reaction space’ for a particular process. Importantly, this enables the evaluation of a large number of reaction parameters in a relatively small number of experiments. Whilst this technique is routinely applied by process chemists in a wide range of industries, and also by academics working in engineering disciplines,⁷ it is rarely used in academic chemistry. This is in spite of the fact that optimisation of particular reactions is often an extremely time-consuming part of any research project focused on the development of new synthetic methodology. A major reason for this is the lack of expertise in the use of this technique in academia which leads to a significant ‘energy barrier’. A relatively common exception is the use of DoE for reaction optimisation in projects carried out in collaboration with industrial partners.⁵

This pitfall shown in Fig. 1 can readily be avoided using a true DoE approach in which each vertex of reaction space is explored. In combination with a ‘centre point’ experiment this is then used to evaluate the full multi-dimensional reaction space in order to determine where the highest yield can be obtained (Fig. 2). This provides a great deal more information about the behaviour of the reaction from a similar (or potentially smaller) number of experiments than the traditional approach. The DoE study uses standard statistical techniques to model the effect of each variable (and potential interactions between variables) on the reaction outcome. A further benefit of the statistical approach, is that it can provide a built-in

'cross-check' of each of the individual screening reactions, enabling any anomalous results to be readily identified. In the traditional OVAT approach, repetition of each experiment is advisable to ensure reproducibility, or the entire 'optimisation' could be led astray by a single anomalous result.

## Optimisation of new synthetic methodology

Most new synthetic methodology development projects begin with an initial discovery (by design or by serendipity) of reaction conditions which provide the desired product in moderate yield (Table 1). This is then usually followed by an optimisation phase in which the yield of a single reaction is improved by variation of a variety of parameters. This is often extremely time consuming, as many different factors may need to be explored in order to provide good yields of the desired product

<table><caption>**Table 1** Alternative approaches to the optimisation of a newly discovered reaction</caption><thead><tr><th></th><th>Traditional approach</th><th>DoE approach</th></tr></thead><tbody><tr><td rowspan="3">奧
威
恩</td><td>OVAT optimisation of<br/>representative example<br/>~3 expts per factor?</td><td>Initial discovery<br/>DoE optimisation of<br/>representative example<br/>Up to 4 factors: 11 expts<br/>Up to 8 factors: 19 expts</td></tr><tr><td>Exploration of scope<br/>Focus on high-yielding<br/>examples</td><td>Explore the scope with a<br/>diverse range of substrates;<br/>Find limitations of the method</td></tr><tr><td>Avoid challenging substrates<br/>that do not work under the<br/>'optimal conditions'</td><td>2<sup>nd</sup> DoE optimisation of a<br/>'difficult' example to show how<br/>conditions can be adapted to<br/>accommodate more<br/>challenging compounds.</td></tr></tbody></table>

<figure><img src="image_8.png" /></figure>
<figure><img src="image_4.png" /></figure>

from a representative substrate. Assuming experiments are
performed only at high/medium/low values of each factor, this
requires three experiments for each factor investigated. Once
'optimised' conditions are identified, they are then applied to
a selection of substrates. This makes the assumption, however,
that the optimised conditions for one substrate will also be the
best conditions for other compounds studied. This inherently
leads to the selection of substrates which are 'similar' to the
initial one, frequently meaning that largely unfunctionalised/
lipophilic molecules are explored.¹ More 'difficult' examples
(functional groups, polar molecules, sterically hindered com-
pounds) are avoided as they do not work well under the 'opti-
mised conditions', though in fact they may simply require
modification of the conditions in order to give a good yield of
the product.

By switching to a DoE approach, however, much more infor-
mation could be obtained about the reaction at an early stage
of the project. Optimisation of the initial example *via* DoE
should provide greater understanding of the factors under-
pinning the reaction from a comparable number of experiments
to the traditional approach. Using a resolution IV DoE design,
which can identify all important factors and determine
whether interactions between factors are present or not, up to
eight factors can be explored in a total of 19 experiments
(including the required centre points). This also provides a
good understanding of any interactions between factors that
may be present. The scope of these optimised conditions
could then be explored with a selection of substrates as in the
traditional approach. There is no reason to expect that these
conditions will be suitable for all substrates, however,
especially those that contain potentially reactive functionality.

Further benefit can be obtained, therefore, by taking one of
the 'difficult' substrates, which gives a low yield under the
standard conditions, and using a second DoE process to opti-
mise the reaction. As considerable information has already
been obtained from the original optimisation, it is likely that
only a few carefully chosen factors will need to be varied in
order to provide improvements in the yield. This additional
stage of optimisation could serve to greatly increase the poten-
tial value of the reaction. By demonstrating that the new
methodology can be applied to 'difficult' substrates through
modification of the reaction conditions, the authors will
provide a much better understanding of the versatility of the
new reaction that has been developed. Potential end users of
the chemistry will also have a good idea how to adapt the reac-
tion conditions to make it work for the substrates they wish to
employ.

Solvent optimisation using DoE

In the case of simple quantitative factors (catalyst/reagent
loading, temperature, concentration, reaction time, etc.), the
use of DoE is relatively straightforward and can greatly facili-
tate reaction optimisation. Arguably, however, one of the most
important parameters in reaction optimisation is the choice of

solvent. In academia, this is traditionally done very much
through a process of trial and error based on a chemist's pre-
vious laboratory experience, and may lead to the adoption of
environmentally harmful and/or toxic solvents which will serve
to render the resulting methodology much-less attractive to
other potential users. The vast majority of new synthetic
methods employ a relatively small set of common laboratory
solvents during the reaction optimisation phase,‡ and there-
fore do not often explore the full scope of 'solvent space' in
order to find a truly optimised procedure. The optimisation of
solvent *via* DoE is not straightforward, however, as the suit-
ability of a particular solvent may be a consequence of many
different physical properties. This issue can be overcome,
however, by the use of another statistical technique to analyse
the properties of solvents. At around the same time, both
Carlson and Chastrette demonstrated that principal com-
ponent analysis (PCA) can be harnessed to convert a large set
of solvent properties into a much smaller set of numerical
parameters that enable solvents to be incorporated into an
experimental design.⁸ This provides a 'map' of solvent space in
which solvents with similar properties are grouped together.
Solvents are then selected from different areas of the solvent
map in order to explore 'solvent space' in the DoE. *E.g.* if the
full range of solvents is to be explored, a solvent from each
corner/vertex of the map is chosen along with a suitable centre
point. This simple approach then enables the effect of each
principle component on the reaction outcome to be modelled,
and leads to an understanding of which area of solvent space
is optimal for the reaction. Further insight can then be
obtained by a more focused study of solvents within that area.

This approach has been adopted by many chemists in
industry, but the required PCA solvent maps are not readily
available in the public domain. Industrial users typically have
their own proprietary data, and solvent maps that have been
published are either not targeted towards reaction optimi-
sation (e.g. crystallisation)⁹ or are overly complex.¹⁰ Different
solvent properties are important for different reactions, so it is
important that a relatively diverse set of parameters are
included. Important considerations include how solvation of
compounds, reagents and catalysts is achieved, how the
solvent hydrogen bonds with molecules, and how it interacts
with solid materials. In this article we report a new PCA
solvent map specifically designed for use in new chemistry
development, and outline how this PCA map can be used for
identifying alternatives to toxic/undesirable solvents and
applied in combination with DoE for the optimisation of new
synthetic methodology. In industry, the specific properties
used in each solvent map differ from company to company,
but the terms used in the map below have been found to
be widely applicable in many industrial applications of PCA
in DoE.

<footnote>‡Arguably, most reactions that are developed use one of the following ten common laboratory solvents: Et₂O, THF, MeCN, DMF, DMSO, EtOH, MeOH, CH₂Cl₂, PhMe and acetone.</footnote>
from a representative substrate. Assuming experiments are
performed only at high/medium/low values of each factor, this
requires three experiments for each factor investigated. Once
'optimised' conditions are identified, they are then applied to
a selection of substrates. This makes the assumption, however,
that the optimised conditions for one substrate will also be the
best conditions for other compounds studied. This inherently
leads to the selection of substrates which are 'similar' to the
initial one, frequently meaning that largely unfunctionalised/
lipophilic molecules are explored.¹ More 'difficult' examples
(functional groups, polar molecules, sterically hindered com-
pounds) are avoided as they do not work well under the 'opti-
mised conditions', though in fact they may simply require
modification of the conditions in order to give a good yield of
the product.

By switching to a DoE approach, however, much more infor-
mation could be obtained about the reaction at an early stage
of the project. Optimisation of the initial example *via* DoE
should provide greater understanding of the factors under-
pinning the reaction from a comparable number of experiments
to the traditional approach. Using a resolution IV DoE design,
which can identify all important factors and determine
whether interactions between factors are present or not, up to
eight factors can be explored in a total of 19 experiments
(including the required centre points). This also provides a
good understanding of any interactions between factors that
may be present. The scope of these optimised conditions
could then be explored with a selection of substrates as in the
traditional approach. There is no reason to expect that these
conditions will be suitable for all substrates, however,
especially those that contain potentially reactive functionality.

Further benefit can be obtained, therefore, by taking one of
the 'difficult' substrates, which gives a low yield under the
standard conditions, and using a second DoE process to opti-
mise the reaction. As considerable information has already
been obtained from the original optimisation, it is likely that
only a few carefully chosen factors will need to be varied in
order to provide improvements in the yield. This additional
stage of optimisation could serve to greatly increase the poten-
tial value of the reaction. By demonstrating that the new
methodology can be applied to 'difficult' substrates through
modification of the reaction conditions, the authors will
provide a much better understanding of the versatility of the
new reaction that has been developed. Potential end users of
the chemistry will also have a good idea how to adapt the reac-
tion conditions to make it work for the substrates they wish to
employ.

Solvent optimisation using DoE

In the case of simple quantitative factors (catalyst/reagent
loading, temperature, concentration, reaction time, etc.), the
use of DoE is relatively straightforward and can greatly facili-
tate reaction optimisation. Arguably, however, one of the most
important parameters in reaction optimisation is the choice of

solvent. In academia, this is traditionally done very much
through a process of trial and error based on a chemist's pre-
vious laboratory experience, and may lead to the adoption of
environmentally harmful and/or toxic solvents which will serve
to render the resulting methodology much-less attractive to
other potential users. The vast majority of new synthetic
methods employ a relatively small set of common laboratory
solvents during the reaction optimisation phase,‡ and there-
fore do not often explore the full scope of 'solvent space' in
order to find a truly optimised procedure. The optimisation of
solvent *via* DoE is not straightforward, however, as the suit-
ability of a particular solvent may be a consequence of many
different physical properties. This issue can be overcome,
however, by the use of another statistical technique to analyse
the properties of solvents. At around the same time, both
Carlson and Chastrette demonstrated that principal com-
ponent analysis (PCA) can be harnessed to convert a large set
of solvent properties into a much smaller set of numerical
parameters that enable solvents to be incorporated into an
experimental design.⁸ This provides a 'map' of solvent space in
which solvents with similar properties are grouped together.
Solvents are then selected from different areas of the solvent
map in order to explore 'solvent space' in the DoE. *E.g.* if the
full range of solvents is to be explored, a solvent from each
corner/vertex of the map is chosen along with a suitable centre
point. This simple approach then enables the effect of each
principle component on the reaction outcome to be modelled,
and leads to an understanding of which area of solvent space
is optimal for the reaction. Further insight can then be
obtained by a more focused study of solvents within that area.

This approach has been adopted by many chemists in
industry, but the required PCA solvent maps are not readily
available in the public domain. Industrial users typically have
their own proprietary data, and solvent maps that have been
published are either not targeted towards reaction optimi-
sation (e.g. crystallisation)⁹ or are overly complex.¹⁰ Different
solvent properties are important for different reactions, so it is
important that a relatively diverse set of parameters are
included. Important considerations include how solvation of
compounds, reagents and catalysts is achieved, how the
solvent hydrogen bonds with molecules, and how it interacts
with solid materials. In this article we report a new PCA
solvent map specifically designed for use in new chemistry
development, and outline how this PCA map can be used for
identifying alternatives to toxic/undesirable solvents and
applied in combination with DoE for the optimisation of new
synthetic methodology. In industry, the specific properties
used in each solvent map differ from company to company,
but the terms used in the map below have been found to
be widely applicable in many industrial applications of PCA
in DoE.

<footnote>‡Arguably, most reactions that are developed use one of the following ten common laboratory solvents: Et₂O, THF, MeCN, DMF, DMSO, EtOH, MeOH, CH₂Cl₂, PhMe and acetone.</footnote>
<figure><img src="image_5.png" /><figcaption>Scheme 1 DoE optimisation of a multicomponent reaction.</figcaption></figure>

### Example: optimisation of a multicomponent reaction as part of an industrial collaboration

As mentioned above, DoE optimisation is sometimes used as part of industrial-academic collaborative projects when appropriate expertise is available via the industrial partner. As a representative example, we have previously made use of the technique for optimising a multicomponent reaction (MCR) of oxazolidines.¹¹ The process was originally developed as a 3-component reaction of a pre-formed oxazolidine 1, an isocyanide 2 and a carboxylic acid 3, to give the product 4a. The initial procedure involved reaction in refluxing acetonitrile in the presence of a Brønsted acid catalyst (Scheme 1).¹¹ᵃ Under these conditions, however, the lactone byproduct 5a was obtained in significant quantity, especially when using a 2-arylozazolidine. In collaboration with the industrial partners for the project, a DoE optimisation was used to explore the effect of temperature, concentration, reagent loading (carboxylic acid and isocyanide), catalyst loading and solvent on the reaction outcome.

The solvent emerged as the most significant parameter, with the originally chosen solvent (MeCN) promoting the formation of both 4a and 5a. In contrast, the formation of MCR product 4a was strongly promoted in ¹PrOH, which also favoured the formation of the lactone 5a. In addition, a higher loading of Brønsted acid catalyst was shown to promote the formation of lactone 5a, whilst having negligible effect on the formation of the desired product. Thus, by switching the solvent to ¹PrOH and lowering the catalyst loading the selectivity of the reaction could be improved considerably. The findings from the DoE study subsequently enabled us to identify suitable reaction conditions for carrying out the MCR as a four-component reaction in which the oxazolidinone intermediate was generated in situ from reaction of an amino-

alcohol and a carbonyl compound (Scheme 2a). In a second DoE study, alternative reaction conditions (DMSO, 1 eq. TsOH) were identified to give the lactone product in the absence of the carboxylic acid component (Scheme 2b).

### Development of a new solvent PCA map

As can be seen from the above example, the effect of solvent on a reaction outcome can be hugely important, and a DoE study can provide an excellent insight into how the choice of solvent affects the product distribution of a reaction. However, the selection of suitable solvents for a DoE study is reliant on a suitable data set which gives a good overview of ‘solvent space’ for organic chemical reactions. At present this information is not readily accessible to most researchers, making statistical optimisation of reaction solvent effectively inaccessible to many chemists. We therefore set out to address this issue by construction of a suitable solvent map for use in new reaction development.

Key considerations for the choice of solvents to be included were:

1. Availability from major chemical suppliers

2. Cost

3. Boiling point/melting point

4. Diversity of properties

5. Sustainability/safety issues

We also aimed to include all solvents commonly/traditionally used in academic laboratories, even those whose use is highly undesirable (e.g. $CCl_4$, 1,2-dichloroethane) so that suitable alternatives can readily be identified from the solvent map.

A set of 136 solvents was selected to cover a wide range of different solvent properties (Fig. 3). Approximately twenty physical (e.g. melting point, boiling point) and calculated (e.g. Hansen solubility parameters)¹² properties of these solvents were then used to construct a PCA map (Fig. 4). The dataset was analysed using Umetrics SIMCA software¹³ to produce a principal component model. Approximately 70% of the solvent properties are modelled effectively using three principal components and 80% are modelled by four principal components (Fig. 5). Evaluation of the PCA map indicates that the first principal component correlates to a large extent with solvent polarity with non-polar solvents having high PCA1 values, and polar solvents grouped towards the lower end of the scale. Similarly, PCA2 approximately correlates with polarisability and PCA3 with hydrogen bonding properties. As can be seen from the overview of the solvent map shown in Fig. 4, there is considerably more variation in solvent properties in terms of the first two principal components with a wide distribution across solvent space (−9 < PC1 < +8; −5 < PC2 < +5). There is much less variation in the third principal component with the vast majority of solvents lying in the range −3 < PC3 < +2. In both plots, there are some notable outliers including water (136), perfluoromethylcyclohexane (117), perfluorohexane (127), trifluoroacetic

<figure><img src="image_6.png" /><figcaption>Scheme 2 Optimised conditions for the formation of the two MCR products 4 and 5.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Scheme 1 DoE optimisation of a multicomponent reaction.</figcaption></figure>

## Example: optimisation of a multicomponent reaction as part of an industrial collaboration

As mentioned above, DoE optimisation is sometimes used as part of industrial-academic collaborative projects when appropriate expertise is available via the industrial partner. As a representative example, we have previously made use of the technique for optimising a multicomponent reaction (MCR) of oxazolidines.¹¹ The process was originally developed as a 3-component reaction of a pre-formed oxazolidine 1, an isocyanide 2 and a carboxylic acid 3, to give the product 4a. The initial procedure involved reaction in refluxing acetonitrile in the presence of a Brønsted acid catalyst (Scheme 1).¹¹ᵃ Under these conditions, however, the lactone byproduct 5a was obtained in significant quantity, especially when using a 2-arylozazolidine. In collaboration with the industrial partners for the project, a DoE optimisation was used to explore the effect of temperature, concentration, reagent loading (carboxylic acid and isocyanide), catalyst loading and solvent on the reaction outcome.

The solvent emerged as the most significant parameter, with the originally chosen solvent (MeCN) promoting the formation of both 4a and 5a. In contrast, the formation of MCR product 4a was strongly promoted in ¹PrOH, which also favoured the formation of the lactone 5a. In addition, a higher loading of Brønsted acid catalyst was shown to promote the formation of lactone 5a, whilst having negligible effect on the formation of the desired product. Thus, by switching the solvent to ¹PrOH and lowering the catalyst loading the selectivity of the reaction could be improved considerably. The findings from the DoE study subsequently enabled us to identify suitable reaction conditions for carrying out the MCR as a four-component reaction in which the oxazolidinone intermediate was generated in situ from reaction of an amino-

alcohol and a carbonyl compound (Scheme 2a). In a second DoE study, alternative reaction conditions (DMSO, 1 eq. TsOH) were identified to give the lactone product in the absence of the carboxylic acid component (Scheme 2b).

## Development of a new solvent PCA map

As can be seen from the above example, the effect of solvent on a reaction outcome can be hugely important, and a DoE study can provide an excellent insight into how the choice of solvent affects the product distribution of a reaction. However, the selection of suitable solvents for a DoE study is reliant on a suitable data set which gives a good overview of ‘solvent space’ for organic chemical reactions. At present this information is not readily accessible to most researchers, making statistical optimisation of reaction solvent effectively inaccessible to many chemists. We therefore set out to address this issue by construction of a suitable solvent map for use in new reaction development.

Key considerations for the choice of solvents to be included were:

1. Availability from major chemical suppliers

2. Cost

3. Boiling point/melting point

4. Diversity of properties

5. Sustainability/safety issues

We also aimed to include all solvents commonly/traditionally used in academic laboratories, even those whose use is highly undesirable (e.g. $CCl_4$, 1,2-dichloroethane) so that suitable alternatives can readily be identified from the solvent map.

A set of 136 solvents was selected to cover a wide range of different solvent properties (Fig. 3). Approximately twenty physical (e.g. melting point, boiling point) and calculated (e.g. Hansen solubility parameters)¹² properties of these solvents were then used to construct a PCA map (Fig. 4). The dataset was analysed using Umetrics SIMCA software¹³ to produce a principal component model. Approximately 70% of the solvent properties are modelled effectively using three principal components and 80% are modelled by four principal components (Fig. 5). Evaluation of the PCA map indicates that the first principal component correlates to a large extent with solvent polarity with non-polar solvents having high PCA1 values, and polar solvents grouped towards the lower end of the scale. Similarly, PCA2 approximately correlates with polarisability and PCA3 with hydrogen bonding properties. As can be seen from the overview of the solvent map shown in Fig. 4, there is considerably more variation in solvent properties in terms of the first two principal components with a wide distribution across solvent space (−9 < PC1 < +8; −5 < PC2 < +5). There is much less variation in the third principal component with the vast majority of solvents lying in the range −3 < PC3 < +2. In both plots, there are some notable outliers including water (136), perfluoromethylcyclohexane (117), perfluorohexane (127), trifluoroacetic

<figure><img src="image_10.png" /><figcaption>Scheme 2 Optimised conditions for the formation of the two MCR products 4 and 5.</figcaption></figure>

<figure><img src="image_11.png" /></figure>
1. 1,1,3,3-tetramethylurea

2. 1,2-dichloroethane

3. 1,2-dimethoxyethane

4. 1,3-dimethylimidazolidin-2-one

5. 1,3-propanediol

6. 1,4-dioxane

7. 1-butanol

8. 1-ethyl-2-pyrrolidinone

9. 1-methylimidazole

10. 2,2,2-trifluoroethanol

11. 2,6-dimethylpyridine

12. 2-aminoethanol

13. 2-butanol

14. 2-butyl acetate

15. 2-ethyl-1-butanol

16. 2-methyl-1-butanol

17. 2-methyl-1-pentanol

18. 2-methylbutan-2-ol

19. 2-methylpropan-1-ol

20. 2-methylpropan-2-ol

21. 2-methyltetrahydrofuran

22. 2-pentanone

23. 2-propanol

24. 3,3-dimethyl-2-butanone

25. 3-methyl-3-pentanol

26. 3-pentanol

27. 3-pentanone

28. 4-formylmorpholine

29. 4-methyl-2-pentanol

30. 4-methyltetrahydropyran

31. acetic acid

32. acetic anhydride

33. acetone

34. acetonitrile

35. acetophenone

36. anisole

37. benzene

38. benzonitrile

39. benzotrifluoride

40. benzyl alcohol

41. butanenitrile

42. butyl acetate

43. butyl butyrate

44. carbon disulfide

45. carbon tetrachloride

46. chlorobenzene

47. chloroform

48. cis-decalin

49. cyclohexane

50. cyclopentyl methyl ether

51. decane

52. dichloromethane

53. diethyl ether

54. diethylamine

55. diethylcarbonate

56. dimethyl carbonate

57. dimethyl isosorbide

58. dimethylsulfoxide

59. di-n-butylether

60. di-n-propylether

61. dipentene

62. dipentyl ether

63. dipropylene glycol dimethyl ether (mixture)

64. di-tert-butyl ketone

65. ethanol

66. ethyl acetate

67. ethyl amyl ketone

68. ethyl butanoate

69. ethyl butyl ketone

70. ethyl formate

71. ethyl n-butyl ether

72. ethylene carbonate

73. ethylene glycol

74. ethylphenyl ether

75. fluorobenzene

76. formamide

77. formic acid

78. glycerol

79. glycerol carbonate

80. glycerol-1,3-dimethyl ether

81. glycerol-1-monobutylether

82. glycerol-1-monomethylether

83. heptane

84. hexafluorobenzene

85. hexamethylphosphoramide

86. hexane

87. isobutyl acetate

88. isopentyl acetate

89. isopropyl acetate

90. lactic acid, ethyl ester

91. mesitylene

92. methanesulfonic acid

93. methanol

94. methyl acetate

95. methyl amyl acetate

96. methyl ethyl ketone

97. methyl formate

98. methyl isoamyl ketone

99. methyl isobutyl ketone

100. methyl isobutyrate

101. methylcyclohexane

102. methyl-t-butyl ether

103. m-xylene

104. N,N'-dimethylpropyleneurea

105. N,N-dimethylacetamide

106. N,N-dimethylformamide

107. n-butyl methyl ether

108. n-butylpyrrolidinone

109. nitrobenzene

110. nitromethane

111. N-methylformamide

112. N-methylpyrrolidine-2-one

113. n-propyl acetate

114. o-xylene

115. pentane

116. pentanol

117. perfluoromethylcyclohexane

118. propanol

119. propionitrile

120. propylene carbonate

121. p-xylene

122. pyridine

123. pyrrolidine

124. quinoline

125. sulfolane

126. tert-butyl acetate

127. tetradecafluorohexane

128. tetrahydrofuran

129. tetrahydropyran

130. tetralin

131. toluene

132. triethylamine

133. trifluoroacetic acid

134. trimethyl orthoformate

135. tripropylamine

136. water

<caption>Fig. 3 List of solvents used to create the PCA solvent map.</caption>

acid (133) and hexafluorobenzene (84). In order to apply this type of PCA map in a DoE study, a simplistic model is used in which each principle component is modelled as a separate factor in the design. The exact PC values of the solvents are not used in the design, just their approximate position on the map. Solvents are selected to represent a high (+1) and low (-1) value of each principle component; an additional ‘centre point’ solvent is also chosen which approximately occupies the middle of the solvent space being investigated (0). Thus, to explore the full range of solvent space in three dimensions, eight solvents at the vertices of a cube are chosen, along with a single centre point (Fig. 6). A basic investigation of the effect of solvent on a reaction can be carried out effectively using only two principle components, depending on which factors (polarity, polarisability or hydrogen bonding interactions) are the most important for the reaction being studied. In this case, only five solvents are used, one from each ‘corner’ of solvent space and a centre point. In either case, the use of the solvent map to select the solvents for the DoE study ensures that they have diverse properties across the 2/3 principle components.

Suitable solvents on the PCA map which can be used as the vertices for a full exploration of solvent space, or corner points for a two-dimensional study of the first two principle components are shown in Table 2. Alternatively, only a subsection of solvent space can be explored: e.g. polar aprotic solvents; non-polar solvents. This can be achieved by selecting

solvents at the vertices of a distorted cuboid (or corners of a distorted rectangle) covering the relevant area of solvent space.

## Applications of the solvent map: solvent substitutions

The choice of a solvent for a chemical reaction has a major impact on the environmental impact of the process. Typically, solvent accounts for a significant proportion of the waste produced during a chemical synthesis, and has a major impact on the energy used, both during the reaction itself and during removal/recovery of the solvent after the reaction. As a consequence, the selection of solvents for industrial processes is highly important. Consequently, many different solvent selection guides have been reported by industrial researchers,¹⁴ which take into account a range of important factors including toxicity, safety, environmental impact, ease of recycling/recovery, and sustainability.¹⁵ It has also been noted that the solvents selected for use by medicinal chemists at an early stage of the drug discovery pipeline influence the solvents employed in subsequent process chemistry scale-up routes, and the pharmaceutical industry has made a considerable effort to encourage medicinal chemists to make use of greener and safer solvents when preparing drug candidate molecules. It would also be beneficial if academic chemists involved in the

<figure><img src="image_7.png" /></figure>
1. 1,1,3,3-tetramethylurea

2. 1,2-dichloroethane

3. 1,2-dimethoxyethane

4. 1,3-dimethylimidazolidin-2-one

5. 1,3-propanediol

6. 1,4-dioxane

7. 1-butanol

8. 1-ethyl-2-pyrrolidinone

9. 1-methylimidazole

10. 2,2,2-trifluoroethanol

11. 2,6-dimethylpyridine

12. 2-aminoethanol

13. 2-butanol

14. 2-butyl acetate

15. 2-ethyl-1-butanol

16. 2-methyl-1-butanol

17. 2-methyl-1-pentanol

18. 2-methylbutan-2-ol

19. 2-methylpropan-1-ol

20. 2-methylpropan-2-ol

21. 2-methyltetrahydrofuran

22. 2-pentanone

23. 2-propanol

24. 3,3-dimethyl-2-butanone

25. 3-methyl-3-pentanol

26. 3-pentanol

27. 3-pentanone

28. 4-formylmorpholine

29. 4-methyl-2-pentanol

30. 4-methyltetrahydropyran

31. acetic acid

32. acetic anhydride

33. acetone

34. acetonitrile

35. acetophenone

36. anisole

37. benzene

38. benzonitrile

39. benzotrifluoride

40. benzyl alcohol

41. butanenitrile

42. butyl acetate

43. butyl butyrate

44. carbon disulfide

45. carbon tetrachloride

46. chlorobenzene

47. chloroform

48. cis-decalin

49. cyclohexane

50. cyclopentyl methyl ether

51. decane

52. dichloromethane

53. diethyl ether

54. diethylamine

55. diethylcarbonate

56. dimethyl carbonate

57. dimethyl isosorbide

58. dimethylsulfoxide

59. di-n-buty lether

60. di-n-propylether

61. dipentene

62. dipentyl ether

63. dipropylene glycol dimethyl ether (mixture)

64. di-tert-butyl ketone

65. ethanol

66. ethyl acetate

67. ethyl amyl ketone

68. ethyl butanoate

69. ethyl butyl ketone

70. ethyl formate

71. ethyl n-butyl ether

72. ethylene carbonate

73. ethylene glycol

74. ethylphenyl ether

75. fluorobenzene

76. formamide

77. formic acid

78. glycerol

79. glycerol carbonate

80. glycerol-1,3-dimethyl ether

81. glycerol-1-monobutylether

82. glycerol-1-monomethylether

83. heptane

84. hexafluorobenzene

85. hexamethylphosphoramide

86. hexane

87. isobutyl acetate

88. isopentyl acetate

89. isopropyl acetate

90. lactic acid, ethyl ester

91. mesitylene

92. methanesulfonic acid

93. methanol

94. methyl acetate

95. methyl amyl acetate

96. methyl ethyl ketone

97. methyl formate

98. methyl isoamyl ketone

99. methyl isobutyl ketone

100. methyl isobutyrate

101. methylcyclohexane

102. methyl-t-butyl ether

103. m-xylene

104. N,N'-dimethylpropyleneurea

105. N,N-dimethylacetamide

106. N,N-dimethylformamide

107. n-butyl methyl ether

108. n-butylpyrrolidinone

109. nitrobenzene

110. nitromethane

111. N-methylformamide

112. N-methylpyrrolidine-2-one

113. n-propyl acetate

114. o-xylene

115. pentane

116. pentanol

117. perfluoromethylcyclohexane

118. propanol

119. propionitrile

120. propylene carbonate

121. p-xylene

122. pyridine

123. pyrrolidine

124. quinoline

125. sulfolane

126. tert-butyl acetate

127. tetradecafluorohexane

128. tetrahydrofuran

129. tetrahydropyran

130. tetralin

131. toluene

132. triethylamine

133. trifluoroacetic acid

134. trimethyl orthoformate

135. tripropylamine

136. water

<caption>Fig. 3 List of solvents used to create the PCA solvent map.</caption>

acid (133) and hexafluorobenzene (84). In order to apply this type of PCA map in a DoE study, a simplistic model is used in which each principle component is modelled as a separate factor in the design. The exact PC values of the solvents are not used in the design, just their approximate position on the map. Solvents are selected to represent a high (+1) and low (-1) value of each principle component; an additional ‘centre point’ solvent is also chosen which approximately occupies the middle of the solvent space being investigated (0). Thus, to explore the full range of solvent space in three dimensions, eight solvents at the vertices of a cube are chosen, along with a single centre point (Fig. 6). A basic investigation of the effect of solvent on a reaction can be carried out effectively using only two principle components, depending on which factors (polarity, polarisability or hydrogen bonding interactions) are the most important for the reaction being studied. In this case, only five solvents are used, one from each ‘corner’ of solvent space and a centre point. In either case, the use of the solvent map to select the solvents for the DoE study ensures that they have diverse properties across the 2/3 principle components.

Suitable solvents on the PCA map which can be used as the vertices for a full exploration of solvent space, or corner points for a two-dimensional study of the first two principle components are shown in Table 2. Alternatively, only a subsection of solvent space can be explored: e.g. polar aprotic solvents; non-polar solvents. This can be achieved by selecting

solvents at the vertices of a distorted cuboid (or corners of a distorted rectangle) covering the relevant area of solvent space.

## Applications of the solvent map: solvent substitutions

The choice of a solvent for a chemical reaction has a major impact on the environmental impact of the process. Typically, solvent accounts for a significant proportion of the waste produced during a chemical synthesis, and has a major impact on the energy used, both during the reaction itself and during removal/recovery of the solvent after the reaction. As a consequence, the selection of solvents for industrial processes is highly important. Consequently, many different solvent selection guides have been reported by industrial researchers,¹⁴ which take into account a range of important factors including toxicity, safety, environmental impact, ease of recycling/recovery, and sustainability.¹⁵ It has also been noted that the solvents selected for use by medicinal chemists at an early stage of the drug discovery pipeline influence the solvents employed in subsequent process chemistry scale-up routes, and the pharmaceutical industry has made a considerable effort to encourage medicinal chemists to make use of greener and safer solvents when preparing drug candidate molecules. It would also be beneficial if academic chemists involved in the

<figure><img src="image_12.png" /></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 4 The PCA solvent map; for full details see the ESI.†</figcaption></figure>

development of new synthetic methodology were to take these considerations into account, and explore the use of potentially safer alternative solvents rather than routinely relying on a limited range of ‘traditional’ solvents.

A simple application of the solvent map is to identify alternative solvents for a reaction of interest. This can be particularly useful for substituting highly toxic or otherwise undesirable solvents. In Table 3, a list of potential substitutes

<figure><img src="image_9.png" /></figure>
<figure><img src="image_13.png" /><figcaption>Fig. 4 The PCA solvent map; for full details see the ESI.†</figcaption></figure>

development of new synthetic methodology were to take these considerations into account, and explore the use of potentially safer alternative solvents rather than routinely relying on a limited range of ‘traditional’ solvents.

A simple application of the solvent map is to identify alternative solvents for a reaction of interest. This can be particularly useful for substituting highly toxic or otherwise undesirable solvents. In Table 3, a list of potential substitutes

<figure><img src="image_14.png" /></figure>
<figure><img src="image_10.png" /><figcaption>Fig. 5 Fit of data in the model used to generate the PCA solvent map.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 6 The use of solvent space in a DoE study requires the identification of a solvent approximately located at each vertex of a cube spanning the area of solvent space to be investigated.</figcaption></figure>

<table><caption>**Table 3** Possible alternatives to toxic/hazardous solvents suggested by the solvent map¹⁹</caption><thead><tr><th>Solvent</th><th>Possible alternatives</th></tr></thead><tbody><tr><td>CH<sub>2</sub>Cl<sub>2</sub> (52)</td><td>1,4-Dioxane (6)<br>Dimethyl carbonate (56)<br>4-Methyltetrahydropyran (30)</td></tr><tr><td>CHCl<sub>3</sub> (47)</td><td>Fluorobenzene (75)<br>Trifluorotoluene (39)</td></tr><tr><td>Cl(CH<sub>2</sub>)<sub>2</sub>Cl (2)</td><td>Fluorobenzene (75)<br>Trifluorotoluene (39)</td></tr><tr><td>CCl<sub>4</sub> (45)</td><td>Trifluorotoluene (39)<br>Decalin (48)</td></tr><tr><td>Benzene (37)</td><td>p-Xylene (121)<br>m-Xylene (103)<br>o-Xylene (114)<br>Toluene (131)<br>Fluorobenzene (75)<br>Dipropylene glycol dimethyl ether (63)</td></tr><tr><td>DMSO (58)</td><td>1-Methylimidazole (9)<br>4-Formylmorpholine (28)<br>N-Methylpyrrolidinone (112)<br>1,3-Dimethylimidazolidin-2-one (4)<br>Ethylene carbonate (72)</td></tr><tr><td>DMF (106)</td><td>N,N-Dimethylacetamide (105)<br>Pyridine (122)<br>Tetramethylurea (1)<br>N-Methylpyrrolidinone (112)<br>1-Methylimidazole (9)</td></tr><tr><td>HMPA (85)</td><td>DMPU (104)<br>1-Ethyl-2-pyrrolidinone (8)<br>1,3-Dimethylimidazolidin-2-one (4)<br>N-Methylpyrrolidinone (112)<br>Quinoline (124)</td></tr></tbody></table>

for a selection of hazardous solvents is provided. Thus, carbon tetrachloride, which is still often used in radical reactions despite being heavily restricted as an ozone-depleting chemical, can potentially be substituted with trifluorotoluene. Similarly, trifluorotoluene or fluorobenzene can also be used as alternatives to the toxic solvents chloroform and 1,2-dichloro-

<table><caption>**Table 2** Corner/vertex solvents for use in a full DoE exploration of 'solvent space'</caption><thead><tr><th>Corner</th><th>Vertex</th><th>Solvent</th></tr></thead><tbody><tr><td>0</td><td>0</td><td>1,4-Dioxane (6); 2-ethyl-1-butanol (15); 4-methyltetrahydropyran (30); acetic anhydride (32); methyl isobutyrate (100); toluene (131); trimethyl orthoformate (134); 3-pentanone (27); butanenitrile (41); butyl acetate (42); ethyl butanoate (68); n-propyl acetate (113)</td></tr><tr><td></td><td>1</td><td>2-Butanol (13); 2-methyl-1-butanol (18); 2-methyl-1-pentanol (17); 2-methylpropan-1-ol (19); 2-methylpropan-2-ol (20); 2-propanol (23); 3-pentanol (26); 1-pentanol (116); 1-propanol (118); propionitrile (119)</td></tr><tr><td>1</td><td>2</td><td>1,3-Propanediol (5); 2,2,2-trifluoroethanol (10); acetic acid (31); ethylene glycol (73); formic acid (77); methanol (93); trifluoroacetic acid (133); water (136)</td></tr><tr><td></td><td>3</td><td>1,1,3,3-Tetramethylurea (1); 1,3-dimethylimidazolidin-2-one (4); 1-ethyl-2-pyrrolidinone (8); 1-methylimidazole (9); dimethylsulfoxide (58); hexamethylphosphoramide (85); N,N'-dimethylpropyleneurea (104); N,N'-dimethylacetamide (105); N-methylpyrrolidine-2-one (112); pyridine (122)</td></tr><tr><td>2</td><td>4</td><td>Benzyl alcohol (40); ethylene carbonate (72); formamide (76); glycerol (78); glycerol carbonate (79); glycerol-1-monobutylether (81); methanesulfonic acid (92); nitrobenzene (109); propylene carbonate (120); sulfolane (125)</td></tr><tr><td></td><td>5</td><td>1,2-Dimethoxyethane (3); 2-methyltetrahydrofuran (21); diethyl ether (53); diethylamine (54); di-n-propylether (60); ethyl n-butyl ether (71); methyl-t-butyl ether (102); n-butyl methyl ether (107); trimethylamine (132)</td></tr><tr><td>3</td><td>6</td><td>Heptane (83); hexane (86); methylcyclohexane (101); pentane (115); tert-butyl acetate (126)</td></tr><tr><td></td><td>7</td><td>Dipentene (limonene) (61); di-tert-butyl ketone (64); ethyl amyl ketone (67); dipentyl ether (62)</td></tr><tr><td>4</td><td>8</td><td>Benzene (37); benzotrifluoride (39); carbon disulphide (44); carbon tetrachloride (45); chlorobenzene (46); cis-decalin (48); decane (51); fluorobenzene (75); hexafluorobenzene (84); mesitylene (91); m-xylene (103); o-xylene (114); perfluoromethylcyclohexane (117); p-xylene (121); tetradecafluorohexane (127); tetralin (130).</td></tr></tbody></table>

<figure><img src="image_12.png" /></figure>
<figure><img src="image_15.png" /><figcaption>Fig. 5 Fit of data in the model used to generate the PCA solvent map.</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Fig. 6 The use of solvent space in a DoE study requires the identification of a solvent approximately located at each vertex of a cube spanning the area of solvent space to be investigated.</figcaption></figure>

<table><caption>**Table 3** Possible alternatives to toxic/hazardous solvents suggested by the solvent map¹⁹</caption><thead><tr><th>Solvent</th><th>Possible alternatives</th></tr></thead><tbody><tr><td>CH<sub>2</sub>Cl<sub>2</sub> (52)</td><td>1,4-Dioxane (6)<br>Dimethyl carbonate (56)<br>4-Methyltetrahydropyran (30)</td></tr><tr><td>CHCl<sub>3</sub> (47)</td><td>Fluorobenzene (75)<br>Trifluorotoluene (39)</td></tr><tr><td>Cl(CH<sub>2</sub>)<sub>2</sub>Cl (2)</td><td>Fluorobenzene (75)<br>Trifluorotoluene (39)</td></tr><tr><td>CCl<sub>4</sub> (45)</td><td>Trifluorotoluene (39)<br>Decalin (48)</td></tr><tr><td>Benzene (37)</td><td>p-Xylene (121)<br>m-Xylene (103)<br>o-Xylene (114)<br>Toluene (131)<br>Fluorobenzene (75)<br>Dipropylene glycol dimethyl ether (63)</td></tr><tr><td>DMSO (58)</td><td>1-Methylimidazole (9)<br>4-Formylmorpholine (28)<br>N-Methylpyrrolidinone (112)<br>1,3-Dimethylimidazolidin-2-one (4)<br>Ethylene carbonate (72)</td></tr><tr><td>DMF (106)</td><td>N,N-Dimethylacetamide (105)<br>Pyridine (122)<br>Tetramethylurea (1)<br>N-Methylpyrrolidinone (112)<br>1-Methylimidazole (9)</td></tr><tr><td>HMPA (85)</td><td>DMPU (104)<br>1-Ethyl-2-pyrrolidinone (8)<br>1,3-Dimethylimidazolidin-2-one (4)<br>N-Methylpyrrolidinone (112)<br>Quinoline (124)</td></tr></tbody></table>

for a selection of hazardous solvents is provided. Thus, carbon tetrachloride, which is still often used in radical reactions despite being heavily restricted as an ozone-depleting chemical, can potentially be substituted with trifluorotoluene. Similarly, trifluorotoluene or fluorobenzene can also be used as alternatives to the toxic solvents chloroform and 1,2-dichloro-

<table><caption>**Table 2** Corner/vertex solvents for use in a full DoE exploration of 'solvent space'</caption><thead><tr><th>Corner</th><th>Vertex</th><th>Solvent</th></tr></thead><tbody><tr><td>0</td><td>0</td><td>1,4-Dioxane (6); 2-ethyl-1-butanol (15); 4-methyltetrahydropyran (30); acetic anhydride (32); methyl isobutyrate (100); toluene (131); trimethyl orthoformate (134); 3-pentanone (27); butanenitrile (41); butyl acetate (42); ethyl butanoate (68); n-propyl acetate (113)</td></tr><tr><td></td><td>1</td><td>2-Butanol (13); 2-methyl-1-butanol (18); 2-methyl-1-pentanol (17); 2-methylpropan-1-ol (19); 2-methylpropan-2-ol (20); 2-propanol (23); 3-pentanol (26); 1-pentanol (116); 1-propanol (118); propionitrile (119)</td></tr><tr><td>1</td><td>2</td><td>1,3-Propanediol (5); 2,2,2-trifluoroethanol (10); acetic acid (31); ethylene glycol (73); formic acid (77); methanol (93); trifluoroacetic acid (133); water (136)</td></tr><tr><td></td><td>3</td><td>1,1,3,3-Tetramethylurea (1); 1,3-dimethylimidazolidin-2-one (4); 1-ethyl-2-pyrrolidinone (8); 1-methylimidazole (9); dimethylsulfoxide (58); hexamethylphosphoramide (85); N,N'-dimethylpropyleneurea (104); N,N'-dimethylacetamide (105); N-methylpyrrolidine-2-one (112); pyridine (122)</td></tr><tr><td>2</td><td>4</td><td>Benzyl alcohol (40); ethylene carbonate (72); formamide (76); glycerol (78); glycerol carbonate (79); glycerol-1-monobutylether (81); methanesulfonic acid (92); nitrobenzene (109); propylene carbonate (120); sulfolane (125)</td></tr><tr><td></td><td>5</td><td>1,2-Dimethoxyethane (3); 2-methyltetrahydrofuran (21); diethyl ether (53); diethylamine (54); di-n-propylether (60); ethyl n-butyl ether (71); methyl-t-butyl ether (102); n-butyl methyl ether (107); trimethylamine (132)</td></tr><tr><td>3</td><td>6</td><td>Heptane (83); hexane (86); methylcyclohexane (101); pentane (115); tert-butyl acetate (126)</td></tr><tr><td></td><td>7</td><td>Dipentene (limonene) (61); di-tert-butyl ketone (64); ethyl amyl ketone (67); dipentyl ether (62)</td></tr><tr><td>4</td><td>8</td><td>Benzene (37); benzotrifluoride (39); carbon disulphide (44); carbon tetrachloride (45); chlorobenzene (46); cis-decalin (48); decane (51); fluorobenzene (75); hexafluorobenzene (84); mesitylene (91); m-xylene (103); o-xylene (114); perfluoromethylcyclohexane (117); p-xylene (121); tetradecafluorohexane (127); tetralin (130).</td></tr></tbody></table>

<figure><img src="image_17.png" /></figure>
<figure><img src="image_13.png" /><figcaption>**Scheme 3** Replacement of toxic/hazardous chlorinated solvents with safer alternatives; <sup>a</sup>isolated yield; <sup>b</sup><sup>1</sup>H NMR yield using an internal standard.</figcaption></figure>

ethane, the latter often being used in a variety of metal-catalysed transformations as a higher boiling point alternative to dichloromethane. A number of more attractive alternatives to dichloromethane itself can also be identified from the map including 1,4-dioxane, 4-methyltetrahydropyran and dimethyl carbonate, the latter having very good environmental credentials.<sup>16</sup> A selection of alternatives to benzene and to dipolar aprotic solvents such as DMF, DMSO and HMPA are also provided, though it is acknowledged that many of these alternatives are already widely used in this context.

In order to test the use of the solvent map for substituting chlorinated solvents, we explored alternative solvents for some recently developed gold-catalysed reactions (Scheme 3). The gold-catalysed cyclisation of alkynyl boronic acid **6** to boron enolate **7**, originally developed in dichloromethane,<sup>17</sup> was shown to take place equally effectively in dimethyl carbonate, a solvent with a considerably better safety profile (a). Similarly, the gold-catalysed hydroamination of cyclohexadiene **8**, originally reported in 1,2-dichloroethane,<sup>18</sup> could be carried out effectively in trifluorotoluene or fluorobenzene, the latter proving to be a much better solvent for this particular reaction (b). In both of the solvent substitution reactions shown in Scheme 3, no significant lowering of the reaction yield was observed on replacing the undesirable solvent with a safer/greener alternative. This suggests that these alternatives to chlorinated solvents should be routinely screened by researchers during reaction development, as this could significantly reduce the use of chlorinated solvents by their avoidance at an early stage of the process.

## Case study – DoE optimisation of an S<sub>N</sub>Ar reaction

As part of an ongoing medicinal chemistry project, we needed to carry out the reaction of 4-chloropyrimidine **11** and 3-amino-pyrazole **12** to give **13a** (Scheme 4). A closely related litera-

<figure><img src="image_14.png" /><figcaption>**Scheme 4** DoE optimisation of an S<sub>N</sub>Ar reaction in DMF.</figcaption></figure>

ture procedure was used as a basis for carrying out the reaction, using DIPEA as a base and NaI as an additive in DMF.<sup>20</sup> Microwave heating at high temperature was used to give a reasonable conversion of the starting materials, but this led to formation of multiple reaction products. As well as the desired product **13a**, the two regioisomeric S<sub>N</sub>Ar products **13b**§ and **13c**§ were also formed alongside an initially unidentified byproduct **13d**. This was later determined to be the product of an S<sub>N</sub>Ar reaction between dimethylamine (generated from decomposition of DMF at high temperature) and the chloropyrimidine **11**. In all of the experiments carried out in the initial study, the isolated yield of product **13a** obtained was less than 20%.

The selection of factors and ranges for a DoE study is of great importance, as poor choices can limit the utility of the exercise. Thus, it is essential to select wide-enough ranges for each factor which enable the design to explore a sufficiently large area of 'reaction space'. However, for useful information to be gained, the reaction should still work (i.e. give a non-zero yield of the product) at the extreme edges of the design space. For an initial DoE study,<sup>21</sup> we elected to examine the effect of varying the quantity of **12**, DIPEA (1–5 eq.) and sodium iodide (0.1–2.0 eq.), alongside the reaction concentration (2–5 mL of DMF) and the temperature (120–200 °C). This was carried out via a total of 16 experiments plus three centre points to enable the effect of the factors to be determined. This enabled the factors favouring the formation of each of the different reaction products to be elucidated. The centre points are three reactions performed under identical conditions at the centre of the design space (i.e. the mid-point of all of the factor ranges) which provide an indication of the reproducibility of the reaction. Performing the reaction under identical conditions should of course give an identical outcome, but there are inevitably some errors in the experimental/analytical procedures which can lead to variation of the yields. It is therefore important to plan the design carefully to minimise any potential errors. For example, preparing a solution of a reagent of

<footnote>§CCDC 1423524 and 1423525 contain the supplementary crystallographic data for compounds **13b** and **13c**.</footnote>
<figure><img src="image_18.png" /><figcaption>**Scheme 3** Replacement of toxic/hazardous chlorinated solvents with safer alternatives; <sup>a</sup>isolated yield; <sup>b</sup><sup>1</sup>H NMR yield using an internal standard.</figcaption></figure>

ethane, the latter often being used in a variety of metal-catalysed transformations as a higher boiling point alternative to dichloromethane. A number of more attractive alternatives to dichloromethane itself can also be identified from the map including 1,4-dioxane, 4-methyltetrahydropyran and dimethyl carbonate, the latter having very good environmental credentials.<sup>16</sup> A selection of alternatives to benzene and to dipolar aprotic solvents such as DMF, DMSO and HMPA are also provided, though it is acknowledged that many of these alternatives are already widely used in this context.

In order to test the use of the solvent map for substituting chlorinated solvents, we explored alternative solvents for some recently developed gold-catalysed reactions (Scheme 3). The gold-catalysed cyclisation of alkynyl boronic acid **6** to boron enolate **7**, originally developed in dichloromethane,<sup>17</sup> was shown to take place equally effectively in dimethyl carbonate, a solvent with a considerably better safety profile (a). Similarly, the gold-catalysed hydroamination of cyclohexadiene **8**, originally reported in 1,2-dichloroethane,<sup>18</sup> could be carried out effectively in trifluorotoluene or fluorobenzene, the latter proving to be a much better solvent for this particular reaction (b). In both of the solvent substitution reactions shown in Scheme 3, no significant lowering of the reaction yield was observed on replacing the undesirable solvent with a safer/greener alternative. This suggests that these alternatives to chlorinated solvents should be routinely screened by researchers during reaction development, as this could significantly reduce the use of chlorinated solvents by their avoidance at an early stage of the process.

## Case study – DoE optimisation of an $S_NAr$ reaction

As part of an ongoing medicinal chemistry project, we needed to carry out the reaction of 4-chloropyrimidine **11** and 3-amino-pyrazole **12** to give **13a** (Scheme 4). A closely related litera-

<figure><img src="image_19.png" /><figcaption>**Scheme 4** DoE optimisation of an $S_NAr$ reaction in DMF.</figcaption></figure>

ture procedure was used as a basis for carrying out the reaction, using DIPEA as a base and NaI as an additive in DMF.<sup>20</sup> Microwave heating at high temperature was used to give a reasonable conversion of the starting materials, but this led to formation of multiple reaction products. As well as the desired product **13a**, the two regioisomeric $S_NAr$ products **13b**§ and **13c**§ were also formed alongside an initially unidentified byproduct **13d**. This was later determined to be the product of an $S_NAr$ reaction between dimethylamine (generated from decomposition of DMF at high temperature) and the chloropyrimidine **11**. In all of the experiments carried out in the initial study, the isolated yield of product **13a** obtained was less than 20%.

The selection of factors and ranges for a DoE study is of great importance, as poor choices can limit the utility of the exercise. Thus, it is essential to select wide-enough ranges for each factor which enable the design to explore a sufficiently large area of ‘reaction space’. However, for useful information to be gained, the reaction should still work (i.e. give a non-zero yield of the product) at the extreme edges of the design space. For an initial DoE study,<sup>21</sup> we elected to examine the effect of varying the quantity of **12**, DIPEA (1–5 eq.) and sodium iodide (0.1–2.0 eq.), alongside the reaction concentration (2–5 mL of DMF) and the temperature (120–200 °C). This was carried out via a total of 16 experiments plus three centre points to enable the effect of the factors to be determined. This enabled the factors favouring the formation of each of the different reaction products to be elucidated. The centre points are three reactions performed under identical conditions at the centre of the design space (i.e. the mid-point of all of the factor ranges) which provide an indication of the reproducibility of the reaction. Performing the reaction under identical conditions should of course give an identical outcome, but there are inevitably some errors in the experimental/analytical procedures which can lead to variation of the yields. It is therefore important to plan the design carefully to minimise any potential errors. For example, preparing a solution of a reagent of

<footnote>§CCDC 1423524 and 1423525 contain the supplementary crystallographic data for compounds **13b** and **13c**.</footnote>
<figure><img src="image_15.png" /></figure>

known concentration and dispensing appropriate volumes of this solution into each experiment will generally provide much greater accuracy than weighing out reagents for each reaction separately. In our case, stock solutions of **11** and **12** were prepared in order to minimise any variation in the amount of limiting reagent present in each reaction. Similarly, it is important to identify a reproducible method for measuring the yield. Early experiments demonstrated that the aqueous work-up of this reaction led to considerable variation in yield of the products **13a–13d**, so in the DoE study all reactions were concentrated directly under vacuum prior to analysis of the crude residue by NMR using an internal standard. For the three centre points this gave fairly consistent yields, as can be seen in Fig. 7 which illustrates the much smaller variation in the replicate experiments (blue) in comparison to the other reactions (green) in terms of the yield of **13a** observed.

Analysis of the results provides details of which factors affect the yield of the desired product **13a**. These are illustrated by the coefficient plot shown in Fig. 8. Each green bar represents a significant factor in the reaction, illustrating the average effect on the yield of **13a** on increasing the factor from

the mid-point of the design to the highest value in the design. Thus, the most significant factor in the yield of **13a** is the temperature, with the higher temperature (200 °C) giving on average a 2.5% increase in the yield. Notably, increasing the amount of NaI to 2 eq. leads, on average, to a 2% decrease in the yield of **13a**, whilst increasing the amount of DMF leads to a 1.7% decrease.

The factors affecting the yields of each of the products **13a–13d** are shown in Table 4. As expected, the NaI additive was not beneficial for the formation of the desired product **13a**, and this was therefore omitted from subsequent reactions. Interestingly, the formation of the regioisomeric $S_{NAr}$ products **13b–13c** is favoured by increasing the quantity of base used in the reaction, whereas the formation of the desired product **13a** is largely unaffected by the amount of base. Furthermore, there is an interaction between the quantity of base used and the temperature: increasing the quantity of base leads to a much larger quantity of the side products **13b** and **13c** at higher temperature. It was therefore concluded that removing the DIPEA entirely in future reactions would be beneficial both in terms of improving the selectivity of the reaction and facilitating purification. During the course of this initial DoE study, a pure sample of the byproduct **13d** was isolated and the structure confirmed. This byproduct is evidently formed through thermal decomposition of the solvent to generate dimethylamine which then undergoes an $S_{NAr}$ reaction with the chloropyrimidine **11**. A switch in solvent was therefore necessary to avoid the formation of **13d**, and we elected to make use of our newly developed PCA solvent map to evaluate an area of solvent space for this transformation, alongside temperature and concentration as the other important variables. We chose to incorporate solvent as a two-dimensional parameter in the design to provide a useful preliminary insight into the effect of the main two solvent principle components on the reaction (*t*1 and *t*2). Although these first two principle components only accurately model 55% of the original solvent properties, this is sufficient to provide an insight into which areas of solvent space are suitable for a particular reaction, and a further more detailed solvent optimisation can then be carried out subsequently if required. Solvents were selected approximately in each quadrant of the map, taking into account the temperature range to be studied, their compatibility with microwave heating and their ability to solubilise the reagents. Dimethyl-

<figure><img src="image_16.png" /><figcaption>Fig. 7 Variation in the yield of **13a** across the 19 experiments in the DoE.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Fig. 8 Factors affecting the yield of **13a** obtained.</figcaption></figure>

<table><caption>Table 4 Factors influencing the formation of the four products **13a–13d**</caption><thead><tr><th>Product</th><th>Favoured by</th><th>Disfavoured by</th></tr></thead><tbody><tr><td>13a</td><td>Increasing temp.</td><td>Increasing NaI<br>Increasing solvent vol.</td></tr><tr><td>13b</td><td>Increasing base<br>Increasing temp.</td><td></td></tr><tr><td>13c</td><td>Increasing base<br>Increasing temp.</td><td></td></tr><tr><td>13d</td><td>Increasing temp.</td><td>Increasing NaI<br>Increasing base</td></tr></tbody></table>
<figure><img src="image_20.png" /></figure>

known concentration and dispensing appropriate volumes of this solution into each experiment will generally provide much greater accuracy than weighing out reagents for each reaction separately. In our case, stock solutions of **11** and **12** were prepared in order to minimise any variation in the amount of limiting reagent present in each reaction. Similarly, it is important to identify a reproducible method for measuring the yield. Early experiments demonstrated that the aqueous work-up of this reaction led to considerable variation in yield of the products **13a–13d**, so in the DoE study all reactions were concentrated directly under vacuum prior to analysis of the crude residue by NMR using an internal standard. For the three centre points this gave fairly consistent yields, as can be seen in Fig. 7 which illustrates the much smaller variation in the replicate experiments (blue) in comparison to the other reactions (green) in terms of the yield of **13a** observed.

Analysis of the results provides details of which factors affect the yield of the desired product **13a**. These are illustrated by the coefficient plot shown in Fig. 8. Each green bar represents a significant factor in the reaction, illustrating the average effect on the yield of **13a** on increasing the factor from

the mid-point of the design to the highest value in the design. Thus, the most significant factor in the yield of **13a** is the temperature, with the higher temperature (200 °C) giving on average a 2.5% increase in the yield. Notably, increasing the amount of NaI to 2 eq. leads, on average, to a 2% decrease in the yield of **13a**, whilst increasing the amount of DMF leads to a 1.7% decrease.

The factors affecting the yields of each of the products **13a–13d** are shown in Table 4. As expected, the NaI additive was not beneficial for the formation of the desired product **13a**, and this was therefore omitted from subsequent reactions. Interestingly, the formation of the regioisomeric $S_{NAr}$ products **13b–13c** is favoured by increasing the quantity of base used in the reaction, whereas the formation of the desired product **13a** is largely unaffected by the amount of base. Furthermore, there is an interaction between the quantity of base used and the temperature: increasing the quantity of base leads to a much larger quantity of the side products **13b** and **13c** at higher temperature. It was therefore concluded that removing the DIPEA entirely in future reactions would be beneficial both in terms of improving the selectivity of the reaction and facilitating purification. During the course of this initial DoE study, a pure sample of the byproduct **13d** was isolated and the structure confirmed. This byproduct is evidently formed through thermal decomposition of the solvent to generate dimethylamine which then undergoes an $S_{NAr}$ reaction with the chloropyrimidine **11**. A switch in solvent was therefore necessary to avoid the formation of **13d**, and we elected to make use of our newly developed PCA solvent map to evaluate an area of solvent space for this transformation, alongside temperature and concentration as the other important variables. We chose to incorporate solvent as a two-dimensional parameter in the design to provide a useful preliminary insight into the effect of the main two solvent principle components on the reaction (t1 and t2). Although these first two principle components only accurately model 55% of the original solvent properties, this is sufficient to provide an insight into which areas of solvent space are suitable for a particular reaction, and a further more detailed solvent optimisation can then be carried out subsequently if required. Solvents were selected approximately in each quadrant of the map, taking into account the temperature range to be studied, their compatibility with microwave heating and their ability to solubilise the reagents. Dimethyl-

<figure><img src="image_21.png" /><figcaption>Fig. 7 Variation in the yield of **13a** across the 19 experiments in the DoE.</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>Fig. 8 Factors affecting the yield of **13a** obtained.</figcaption></figure>

<table><caption>Table 4 Factors influencing the formation of the four products **13a–13d**</caption><thead><tr><th>Product</th><th>Favoured by</th><th>Disfavoured by</th></tr></thead><tbody><tr><td>13a</td><td>Increasing temp.</td><td>Increasing NaI<br>Increasing solvent vol.</td></tr><tr><td>13b</td><td>Increasing base<br>Increasing temp.</td><td></td></tr><tr><td>13c</td><td>Increasing base<br>Increasing temp.</td><td></td></tr><tr><td>13d</td><td>Increasing temp.</td><td>Increasing NaI<br>Increasing base</td></tr></tbody></table>
<figure><img src="image_18.png" /><figcaption>Fig. 9 Solvents selected for screening in the second DoE study.</figcaption></figure>

acetamide (105), 1-butanol (7), cyclopentyl methyl ether (50) and dipropyl ether (60) were selected as 'corner' points, with propionitrile (119) as a centre point (Fig. 9).

The DoE study also included temperature (100–140 °C) and concentration (0.1–0.5 M) as factors, and this required a total of eight experiments plus three centre points to give a resolution IV design in which individual factors are well resolved but interactions between factors are confounded.

As expected, the solvolysis product 13d was not observed in most solvents although it was still formed in one of the high temperature reactions carried out in DMA. An excellent model was obtained using multiple linear regression (MLR) for predicting the yield of the desired product 13a (Fig. 10). The factors affecting the yield of 13a are shown in the coefficient plot (Fig. 11). Temperature and concentration are the most significant factors, with higher temperature and higher concentration leading to an improvement in yield as might be expected. The interactions between factors are not fully resolved using a resolution IV design, so care must be taken in interpreting the results. The solvent dependence is somewhat complicated, with the two principle components potentially showing a significant interaction (though as the interactions are not resolved this interaction between t1 and t2 is confounded with the interaction between temperature and con-

<figure><img src="image_19.png" /><figcaption>Fig. 11 Coefficient plot generated from the MODDE experimental design showing the factors influencing the yield of 13a.</figcaption></figure>

centration; similarly the interaction between *t*1 and concentration is confounded with the interaction between *t*2 and temperature). However, subsequent preparative experi-ments confirmed that the solvent was an important factor.²² Thus, whilst neither principle component is very important as a factor in its own right, there is a strong interaction with the most favourable areas of solvent space being either high *t*1/low *t*2 or low *t*1/high *t*2. This interaction is illustrated by the plot in Fig. 12. This suggests that either DMA or Pr₂O are preferable for the reaction, with the latter being slightly more effective – a somewhat unusual choice of solvent for an S<sub>N</sub>Ar reaction! Further-more, it was observed that the product **13a** (and unreacted amine **12**) often precipitated out of Pr₂O at the end of the reac-tion, facilitating purification of the product. Satisfyingly, by carrying out the reaction at high concentration/temperature in Pr₂O, a 57% isolated yield of product **13a** was obtained (Scheme 5), along with small amounts of products **13b** and **13c** (and 13% recovered starting material). This gave material in sufficient quantity and purity for the project, so no further optimisation was carried out from this point onwards.

<figure><img src="image_20.png" /><figcaption>Fig. 10 Fit of the model obtained for the DoE for the yield of 13a.</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>Fig. 12 Graph showing the interaction between the two solvent principle components (t1 and t2).</figcaption></figure>

<figure><img src="image_22.png" /></figure>
<figure><img src="image_23.png" /><figcaption>Fig. 9 Solvents selected for screening in the second DoE study.</figcaption></figure>

acetamide (105), 1-butanol (7), cyclopentyl methyl ether (50) and dipropyl ether (60) were selected as 'corner' points, with propionitrile (119) as a centre point (Fig. 9).

The DoE study also included temperature (100–140 °C) and concentration (0.1–0.5 M) as factors, and this required a total of eight experiments plus three centre points to give a resolution IV design in which individual factors are well resolved but interactions between factors are confounded.

As expected, the solvolysis product **13d** was not observed in most solvents although it was still formed in one of the high temperature reactions carried out in DMA. An excellent model was obtained using multiple linear regression (MLR) for predicting the yield of the desired product **13a** (Fig. 10). The factors affecting the yield of **13a** are shown in the coefficient plot (Fig. 11). Temperature and concentration are the most significant factors, with higher temperature and higher concentration leading to an improvement in yield as might be expected. The interactions between factors are not fully resolved using a resolution IV design, so care must be taken in interpreting the results. The solvent dependence is somewhat complicated, with the two principle components potentially showing a significant interaction (though as the interactions are not resolved this interaction between *t*1 and *t*2 is confounded with the interaction between temperature and con-

<figure><img src="image_24.png" /><figcaption>Fig. 11 Coefficient plot generated from the MODDE experimental design showing the factors influencing the yield of **13a**.</figcaption></figure>

centration; similarly the interaction between *t*1 and concentration is confounded with the interaction between *t*2 and temperature). However, subsequent preparative experiments confirmed that the solvent was an important factor.²² Thus, whilst neither principle component is very important as a factor in its own right, there is a strong interaction with the most favourable areas of solvent space being either high *t*1/low *t*2 or low *t*1/high *t*2. This interaction is illustrated by the plot in Fig. 12. This suggests that either DMA or Pr₂O are preferable for the reaction, with the latter being slightly more effective – a somewhat unusual choice of solvent for an S<sub>N</sub>Ar reaction! Furthermore, it was observed that the product **13a** (and unreacted amine **12**) often precipitated out of Pr₂O at the end of the reaction, facilitating purification of the product. Satisfyingly, by carrying out the reaction at high concentration/temperature in Pr₂O, a 57% isolated yield of product **13a** was obtained (Scheme 5), along with small amounts of products **13b** and **13c** (and 13% recovered starting material). This gave material in sufficient quantity and purity for the project, so no further optimisation was carried out from this point onwards.

<figure><img src="image_25.png" /><figcaption>Fig. 10 Fit of the model obtained for the DoE for the yield of 13a.</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>Fig. 12 Graph showing the interaction between the two solvent principle components (*t*1 and *t*2).</figcaption></figure>

<figure><img src="image_27.png" /></figure>
<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /><figcaption>Scheme 5 Optimised procedure for the S<sub>N</sub>Ar reaction in Pr<sub>2</sub>O.</figcaption></figure>

Conclusions

The potential benefits of the ‘Design of Experiments’ approach to reaction optimisation for the development of new methodology have been discussed, with a discussion of how the technique can be applied to reactions during their development. A new PCA solvent map has been developed specifically for use in new reaction development incorporating 136 solvents which offer a wide range of different properties. The application of the new PCA solvent map for identifying alternatives to toxic chlorinated solvents has been demonstrated, and it has also been used in the DoE optimisation of an S<sub>N</sub>Ar reaction. Interestingly, it was demonstrated that this reaction works well in solvents in opposite corners of the solvent map – DMA or Pr<sub>2</sub>O. The latter choice was shown to be highly effective both for effective reaction and for ease of purification of the S<sub>N</sub>Ar product, and would most likely not have been considered a viable solvent to test in a traditional ‘optimisation’ study.

Acknowledgements

We would like to acknowledge the Engineering and Physical Sciences Research Council (postdoctoral funding for LB, grant reference EP/K001183/1 and the EPSRC Dial-a-Molecule Network for financial support to TDS/PMM) for financial support. We would also like to thank the UCL PhD Program in Drug Discovery for a PhD studentship (awarded to FB).

Notes and references

1 A. Nadin, C. Hattotuwagama and I. Churcher, *Angew. Chem., Int. Ed.*, 2012, **51**, 1114–1122.

2 (a) P. M. Murray, S. N. G. Tyler and J. D. Moseley, *Org. Process Res. Dev.*, 2013, **17**, 40–46; (b) S. A. Weissman and N. G. Anderson, *Org. Process Res. Dev.*, 2015, **19**, 1605–1633; (c) D. Lendrem, M. Owen and S. Godbert, *Org. Process Res. Dev.*, 2001, **5**, 324–327.

3 (a) H. Tye and M. Whittaker, *Org. Biomol. Chem.*, 2004, **2**, 813–815; (b) S. Stone, T. Wang, J. Liang, J. Cochran, J. Green and W. Gu, *Org. Biomol. Chem.*, 2015, **13**, 10471–10476; (c) G. Guercio, A. Perboni, F. Tinazzi, L. Rovatti and S. Provera, *Org. Process Res. Dev.*, 2010, **14**, 840–848.

4 (a) N. Caldwell, C. Jamieson, I. Simpson and A. J. B. Watson, *ACS Sustainable Chem. Eng.*, 2013, **1**, 1339–1344;

(b) A. L. García-Cabeza, R. Marín-Barrios, R. Azarken, F. J. Moreno-Dorado, M. J. Ortega, H. Vidal, J. M. Gatica, G. M. Massanet and F. M. Guerra, *Eur. J. Org. Chem.*, 2013, 8307–8314; (c) C. C. Perez, J. M. Pena and C. R. D. Correia, *New J. Chem.*, 2014, **38**, 3933–3938; (d) A. R. Alimardanov, M. T. Barrila, F. R. Busch, J. J. Carey, M. A. Couturier and C. Cui, *Org. Process Res. Dev.*, 2004, **8**, 834–837; (e) V. Karaluka, R. M. Lanigan, P. M. Murray, M. Badland and T. D. Sheppard, *Org. Biomol. Chem.*, 2015, **13**, 10888–10894.

5 (a) P. Renzi, C. Kronig, A. Carlone, S. Eröksüz, A. Berkessel and M. Bella, *Chem. – Eur. J.*, 2014, **20**, 11768–11775; (b) A. Ekebergh, C. Lingblom, P. Sandin, C. Wennerås and J. Mårtensson, *Org. Biomol. Chem.*, 2015, **13**, 3382–3392; (c) V. Hajzer, P. Alexy, A. Latika, J. Durmis and R. Šebesta, *Monatsh. Chem.*, 2015, **146**, 1541–1545; (d) C. Jamieson, M. S. Congreve, D. F. Emiabata-Smith, S. V. Ley and J. J. Scicinski, *Org. Process Res. Dev.*, 2002, **6**, 823–825; (e) C. Jamieson, M. S. Congreve, D. F. Emiabata-Smith and S. V. Ley, *Synlett*, 2000, 1603–1607; (f) M. D. Evans, J. Ring, A. Schoen, A. Bell, P. Edwards, D. Berthelot, R. Nicewonger and C. M. Baldino, *Tetrahedron Lett.*, 2003, **44**, 9337–9341; (g) T. N. Glasnow, H. Tye and C. O. Kappe, *Tetrahedron*, 2008, **64**, 2035–2041; (h) See also ref 11b.

6 (a) R. Leardi, *Anal. Chim. Acta*, 2009, **652**, 161–172; (b) R. Carlson and T. Hudlicky, *Helv. Chim. Acta*, 2012, **95**, 2052–2062.

7 (a) C. Grant, A. C. da Silva Damas Pinto, H.-P. Lui, J. M. Woodley and F. Baganz, *Biotechnol. Bioeng.*, 2012, **109**, 2179–2189; (b) R. S. Islam, D. Tisi, M. S. Levy and G. J. Lye, *Biotechnol. Prog.*, 2007, **23**, 785–793.

8 (a) R. Carlson, T. Lundstedt and C. Albano, *Acta Chem. Scand., Ser. B*, 1985, **39**, 79–91; (b) M. Chastrette, M. Rajzmann, M. Chanon and K. F. Purcell, *J. Am. Chem. Soc.*, 1985, **107**, 1–11; (c) R. Carlson and J. E. Carlson, *Org. Process Res. Dev.*, 2005, **9**, 680–689.

9 (a) M. Allesø, F. van den Berg, C. Cornett, F. Steen Jørgensen, B. Halling-Sørensen, H. Lopez de Diego, L. Hovgaard, J. Aaltonen and J. Rantanen, *J. Pharm. Sci.*, 2008, **97**, 2145–2159; (b) M. Allesø, J. Rantanen, J. Aaltonen, C. Cornett and F. van den Berg, *J. Chemom.*, 2008, **22**, 621–631.

10 A. R. Katrizky, D. C. Fara, M. Kuanar, E. Hur and M. Karelson, *J. Phys. Chem. A*, 2005, **109**, 10323–10341.

11 (a) L. J. Diorazio, W. B. Motherwell, T. D. Sheppard and R. W. Waller, *Synlett*, 2006, 2281–2283; (b) R. W. Waller, L. J. Diorazio, B. A. Taylor, W. B. Motherwell and T. D. Sheppard, *Tetrahedron*, 2010, **66**, 6496–6507.

12 Hansen Solubility Parameters: A User's Handbook, CRC Press, Inc., Boca Raton, Florida, 2007.

13 The PCA solvent map was developed using SIMCA software, available from Umetrics; http://www.umetrics.com/products/simca (accessed 24th November 2015).

14 For articles discussing the selection of less toxic/hazardous solvents, see: (a) D. S. MacMillan, J. Murray, H. F. Sneddon, C. Jamieson and A. J. B. Watson, *Green Chem.*, 2013, **15**,
<figure><img src="image_28.png" /><figcaption>**Scheme 5** Optimised procedure for the S<sub>N</sub>Ar reaction in Pr<sub>2</sub>O.</figcaption></figure>

Conclusions

The potential benefits of the ‘Design of Experiments’ approach to reaction optimisation for the development of new methodology have been discussed, with a discussion of how the technique can be applied to reactions during their development. A new PCA solvent map has been developed specifically for use in new reaction development incorporating 136 solvents which offer a wide range of different properties. The application of the new PCA solvent map for identifying alternatives to toxic chlorinated solvents has been demonstrated, and it has also been used in the DoE optimisation of an S<sub>N</sub>Ar reaction. Interestingly, it was demonstrated that this reaction works well in solvents in opposite corners of the solvent map – DMA or Pr<sub>2</sub>O. The latter choice was shown to be highly effective both for effective reaction and for ease of purification of the S<sub>N</sub>Ar product, and would most likely not have been considered a viable solvent to test in a traditional ‘optimisation’ study.

Acknowledgements

We would like to acknowledge the Engineering and Physical Sciences Research Council (postdoctoral funding for LB, grant reference EP/K001183/1 and the EPSRC Dial-a-Molecule Network for financial support to TDS/PMM) for financial support. We would also like to thank the UCL PhD Program in Drug Discovery for a PhD studentship (awarded to FB).

Notes and references

1 A. Nadin, C. Hattotuwagama and I. Churcher, *Angew. Chem., Int. Ed.*, 2012, **51**, 1114–1122.

2 (a) P. M. Murray, S. N. G. Tyler and J. D. Moseley, *Org. Process Res. Dev.*, 2013, **17**, 40–46; (b) S. A. Weissman and N. G. Anderson, *Org. Process Res. Dev.*, 2015, **19**, 1605–1633; (c) D. Lendrem, M. Owen and S. Godbert, *Org. Process Res. Dev.*, 2001, **5**, 324–327.

3 (a) H. Tye and M. Whittaker, *Org. Biomol. Chem.*, 2004, **2**, 813–815; (b) S. Stone, T. Wang, J. Liang, J. Cochran, J. Green and W. Gu, *Org. Biomol. Chem.*, 2015, **13**, 10471–10476; (c) G. Guercio, A. Perboni, F. Tinazzi, L. Rovatti and S. Provera, *Org. Process Res. Dev.*, 2010, **14**, 840–848.

4 (a) N. Caldwell, C. Jamieson, I. Simpson and A. J. B. Watson, *ACS Sustainable Chem. Eng.*, 2013, **1**, 1339–1344;

(b) A. L. García-Cabeza, R. Marín-Barrios, R. Azarken, F. J. Moreno-Dorado, M. J. Ortega, H. Vidal, J. M. Gatica, G. M. Massanet and F. M. Guerra, *Eur. J. Org. Chem.*, 2013, 8307–8314; (c) C. C. Perez, J. M. Pena and C. R. D. Correia, *New J. Chem.*, 2014, **38**, 3933–3938; (d) A. R. Alimardanov, M. T. Barrila, F. R. Busch, J. J. Carey, M. A. Couturier and C. Cui, *Org. Process Res. Dev.*, 2004, **8**, 834–837; (e) V. Karaluka, R. M. Lanigan, P. M. Murray, M. Badland and T. D. Sheppard, *Org. Biomol. Chem.*, 2015, **13**, 10888–10894.

5 (a) P. Renzi, C. Kronig, A. Carlone, S. Eröksüz, A. Berkessel and M. Bella, *Chem. – Eur. J.*, 2014, **20**, 11768–11775; (b) A. Ekebergh, C. Lingblom, P. Sandin, C. Wennerås and J. Mårtensson, *Org. Biomol. Chem.*, 2015, **13**, 3382–3392; (c) V. Hajzer, P. Alexy, A. Latika, J. Durmis and R. Šebesta, *Monatsh. Chem.*, 2015, **146**, 1541–1545; (d) C. Jamieson, M. S. Congreve, D. F. Emiabata-Smith, S. V. Ley and J. J. Scicinski, *Org. Process Res. Dev.*, 2002, **6**, 823–825; (e) C. Jamieson, M. S. Congreve, D. F. Emiabata-Smith and S. V. Ley, *Synlett*, 2000, 1603–1607; (f) M. D. Evans, J. Ring, A. Schoen, A. Bell, P. Edwards, D. Berthelot, R. Nicewonger and C. M. Baldino, *Tetrahedron Lett.*, 2003, **44**, 9337–9341; (g) T. N. Glasnow, H. Tye and C. O. Kappe, *Tetrahedron*, 2008, **64**, 2035–2041; (h) See also ref 11b.

6 (a) R. Leardi, *Anal. Chim. Acta*, 2009, **652**, 161–172; (b) R. Carlson and T. Hudlicky, *Helv. Chim. Acta*, 2012, **95**, 2052–2062.

7 (a) C. Grant, A. C. da Silva Damas Pinto, H.-P. Lui, J. M. Woodley and F. Baganz, *Biotechnol. Bioeng.*, 2012, **109**, 2179–2189; (b) R. S. Islam, D. Tisi, M. S. Levy and G. J. Lye, *Biotechnol. Prog.*, 2007, **23**, 785–793.

8 (a) R. Carlson, T. Lundstedt and C. Albano, *Acta Chem. Scand., Ser. B*, 1985, **39**, 79–91; (b) M. Chastrette, M. Rajzmann, M. Chanon and K. F. Purcell, *J. Am. Chem. Soc.*, 1985, **107**, 1–11; (c) R. Carlson and J. E. Carlson, *Org. Process Res. Dev.*, 2005, **9**, 680–689.

9 (a) M. Allesø, F. van den Berg, C. Cornett, F. Steen Jørgensen, B. Halling-Sørensen, H. Lopez de Diego, L. Hovgaard, J. Aaltonen and J. Rantanen, *J. Pharm. Sci.*, 2008, **97**, 2145–2159; (b) M. Allesø, J. Rantanen, J. Aaltonen, C. Cornett and F. van den Berg, *J. Chemom.*, 2008, 22, 621–631.

10 A. R. Katrizky, D. C. Fara, M. Kuanar, E. Hur and M. Karelson, *J. Phys. Chem. A*, 2005, **109**, 10323–10341.

11 (a) L. J. Diorazio, W. B. Motherwell, T. D. Sheppard and R. W. Waller, *Synlett*, 2006, 2281–2283; (b) R. W. Waller, L. J. Diorazio, B. A. Taylor, W. B. Motherwell and T. D. Sheppard, *Tetrahedron*, 2010, **66**, 6496–6507.

12 Hansen Solubility Parameters: A User's Handbook, CRC Press, Inc., Boca Raton, Florida, 2007.

13 The PCA solvent map was developed using SIMCA software, available from Umetrics; http://www.umetrics.com/products/simca (accessed 24th November 2015).

14 For articles discussing the selection of less toxic/hazardous solvents, see: (a) D. S. MacMillan, J. Murray, H. F. Sneddon, C. Jamieson and A. J. B. Watson, *Green Chem.*, 2013, **15**,

<figure><img src="image_29.png" /></figure>
596–600; (b) H. E. Eastman, C. Jamieson and A. J. B. Watson, *Aldrichimica Acta*, 2015, **48**, 51–55; (c) D. Prat, A. Wells, J. Hayler, H. Sneddon, C. R. McElroy, S. Abou-Shehada and P. J. Dunn, *Green Chem.*, 2015, **45**, DOI: 10.1039/c5gc01008j; (d) A. Benazzouz, L. Moity, C. Pierlot, M. Sergent, V. Molinier and J.-M. Aubry, *Ind. Eng. Chem. Res.*, 2013, **52**, 16585–16597; (e) D. Prat, O. Pardigon, H.-W. Flemming, S. Letestu, V. Ducandas, P. Isnard, E. Guntrum, T. Senac, S. Ruisseau, P. Cruciani and P. Hosek, *Org. Process Res. Dev.*, 2013, **17**, 1517–1525; (f) D. Prat, J. Hayler and A. Wells, *Green Chem.*, 2014, **16**, 4546–4551; (g) R. K. Henderson, C. Jiménez-González, D. J. C. Constable, S. R. Alston, G. G. A. Inglis, G. Fisher, J. Sherwood, S. P. Binks and A. D. Curzons, *Green Chem.*, 2011, **13**, 854–862.

15 (a) P. G. Jessop, *Green Chem.*, 2011, **13**, 1391–1398; (b) C. Jiménez-González, A. D. Curzons, D. J. C. Constable and V. L. Cunningham, *Clean Technol. Environ. Policy*, 2005, 7, 42–50.

16 (a) See ref. 14g; (b) T. Laird, *Org. Process Res. Dev.*, 2012, **16**, 1–2; (c) R. W. Foster, L. Benhamou, M. J. Porter,

D.-K. Bučar, H. C. Hailes, C. J. Tame and T. D. Sheppard, *Chem. – Eur. J.*, 2015, **21**, 6107–6114.

17 C. Körner, P. Starkov and T. D. Sheppard, *J. Am. Chem. Soc.*, 2010, **132**, 5968–5969.

18 C. Brouwer and C. He, *Angew. Chem., Int. Ed.*, 2006, **45**, 1744–1747.

19 Nearest neighbours on the PCA solvent map were identified using the free online PRISM PCA Visualiser available at http://www.prismtc.co.uk/pca-3d-visualiser/ (accessed 24th November 2015).

20 J.-D. Charrier, F. Mazzei, D. Kay and A. Miller, Processes for preparing substituted pyrimidines and pyrimidine derivatives as inhibitors of protein kinase, *WO* 2004000833, 2004.

21 The DoE optimisation was designed and analysed using MODDE 10 software, available from Umetrics; http://www.umetrics.com/products/modde (accessed 24th November 2015).

22 High yields of **13a** were only obtained in either di-*n*-propyl ether or *N*,*N*-dimethylacetamide, with CPME giving consistently low yields. This is consistent with the interaction between *t1* and *t2* shown in Fig. 12.
596–600; (b) H. E. Eastman, C. Jamieson and A. J. B. Watson, *Aldrichimica Acta*, 2015, **48**, 51–55; (c) D. Prat, A. Wells, J. Hayler, H. Sneddon, C. R. McElroy, S. Abou-Shehada and P. J. Dunn, *Green Chem.*, 2015, **45**, DOI: 10.1039/c5gc01008j; (d) A. Benazzouz, L. Moity, C. Pierlot, M. Sergent, V. Molinier and J.-M. Aubry, *Ind. Eng. Chem. Res.*, 2013, **52**, 16585–16597; (e) D. Prat, O. Pardigon, H.-W. Flemming, S. Letestu, V. Ducandas, P. Isnard, E. Guntrum, T. Senac, S. Ruisseau, P. Cruciani and P. Hosek, *Org. Process Res. Dev.*, 2013, **17**, 1517–1525; (f) D. Prat, J. Hayler and A. Wells, *Green Chem.*, 2014, **16**, 4546–4551; (g) R. K. Henderson, C. Jiménez-González, D. J. C. Constable, S. R. Alston, G. G. A. Inglis, G. Fisher, J. Sherwood, S. P. Binks and A. D. Curzons, *Green Chem.*, 2011, **13**, 854–862.

15 (a) P. G. Jessop, *Green Chem.*, 2011, **13**, 1391–1398; (b) C. Jiménez-González, A. D. Curzons, D. J. C. Constable and V. L. Cunningham, *Clean Technol. Environ. Policy*, 2005, 7, 42–50.

16 (a) See ref. 14g; (b) T. Laird, *Org. Process Res. Dev.*, 2012, **16**, 1–2; (c) R. W. Foster, L. Benhamou, M. J. Porter,

D.-K. Bučar, H. C. Hailes, C. J. Tame and T. D. Sheppard, *Chem. – Eur. J.*, 2015, **21**, 6107–6114.

17 C. Körner, P. Starkov and T. D. Sheppard, *J. Am. Chem. Soc.*, 2010, **132**, 5968–5969.

18 C. Brouwer and C. He, *Angew. Chem., Int. Ed.*, 2006, **45**, 1744–1747.

19 Nearest neighbours on the PCA solvent map were identified using the free online PRISM PCA Visualiser available at http://www.prismtc.co.uk/pca-3d-visualiser/ (accessed 24th November 2015).

20 J.-D. Charrier, F. Mazzei, D. Kay and A. Miller, Processes for preparing substituted pyrimidines and pyrimidine derivatives as inhibitors of protein kinase, *WO* 2004000833, 2004.

21 The DoE optimisation was designed and analysed using MODDE 10 software, available from Umetrics; http://www.umetrics.com/products/modde (accessed 24th November 2015).

22 High yields of **13a** were only obtained in either di-*n*-propyl ether or *N*,*N*-dimethylacetamide, with CPME giving consistently low yields. This is consistent with the interaction between *t1* and *t2* shown in Fig. 12.