Robert P Sheridan
email: robert_sheridan@merck.com

Patrick Piras
email: patrick.piras@univ-amu.fr

Edward C Sherer
email: edward_sherer@merck.com

Christian Roussel
email: christian.roussel@univ-amu.fr

William H Pirkle
email: whpirkle@gmail.com

Christopher J Welch
email: christopher_welch@merck.com

Mining Chromatographic Enantioseparation Data Using Matched Molecular Pair Analysis

Keywords: matched molecular pairs, chiral chromatography, chiral recognition Unique Molecules Mean α Stdev α Total Clusters

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Introduction

Finding which type of chiral stationary phase (CSP) will provide the best chromatographic separation for the enantiomers of a given molecule is mostly a trial-and-error process, and since the number of commercial CSPs is now very large, testing a representative sample of columns becomes difficult and time-consuming, even with automated screening of columns. It would be useful to predict in advance which CSP to choose for a particular molecule. There are longstanding rules of thumb to aid in the prediction of enantioseparability for specific CSPs [START_REF] Allenmark | Chromatographic Enantioseparation[END_REF][START_REF] Pirkle | Considerations of chiral recognition relevant to the liquid chromatography separation of enantiomers[END_REF][START_REF] Okamoto | Polysaccharide derivatives for chromatographic separation of enantiomers[END_REF][START_REF] Armstrong | Enantiomeric separations in chromatography[END_REF][START_REF] Francotte | Enantioselective chromatography. An essential and versatile tool for the analytical and preparative separation of enantiomers[END_REF][START_REF] Allenmark | Chromatography on chiral stationary phases[END_REF][START_REF] Lammerhofer | Liquid chromatographic enantiomer separation and chiral recognition by cinchona alkaloid-derived enantioselective separation materials[END_REF], and several attempts have been made over the years to create more broadly applicable statistical models of enantioseparation based on literature data [START_REF] Suzuki | Quantitative structure-enantioselective retention relationships for chromatographic separation of arylalkylcarbinols on Pirkle type chiral stationary phases[END_REF][START_REF] Carotti | LFER and CoMFA studies on optical resolution of -alkyl -aryloxyacetic acid methyl esters on DACH-DNB chiral stationary phase[END_REF][START_REF] Fabian | Modeling the Chromatographic Enantioseparation of Aryl-and Hetarylcarbinols on ULMO, a Brush-Type Chiral Stationary Phase, by 3D-QSAR Techniques[END_REF][START_REF] Del Rio | Enantiophore Modeling in 3D-QSAR. A Data Mining Application on Whelk-O1 Chiral Stationary Phase[END_REF]. Recently, we attempted to generate descriptor-based QSAR (quantitative structure-activity relationship) enantioselectivity models [START_REF] Sheridan | Toward structure-based predictive tools for the selection of chiral stationary phases for the chromatographic separation of enantiomers[END_REF] for a subset of 19 CSPs based on literature data compiled in ChirBase, a database containing more than 200,000 entries of literature-reported chromatographic enantioseparations [START_REF] Roussel | Chirbase: A molecular database for storage and retrieval of chromatographic chiral separations[END_REF]. This attempt was a partial success, leading to the generation of self-consistent QSAR models for four of the 19 CSPs, and allowing for reasonably accurate predictions of enantioseparability for in-house racemic compounds from the Merck sample repository that were not included in the training sets of the models.

The limited ability to find trends in more CSPs using descriptor-based QSAR methods was somewhat disappointing, but conventional QSAR is not the only way to find activity trends. In this paper we discuss the use of matched molecular pair (MMP) methods [START_REF] Dossetter | Matched molecular pair analysis in drug discovery[END_REF] for investigating enantioseparation data. MMP techniques find local chemical changes in similar compounds and look for trends where the same type of chemical change is associated with a consistent change in activity. Trends identified by MMP are complementary to those found by conventional QSAR, mostly because conventional QSAR is looking for features that correlate with absolute low or high activity, whereas MMP is looking for activity differences between molecules that can be anywhere in the activity range. There are two major types of algorithms for identifying matched pairs: ones based on maximum common substructure and ones based on the change of predefined fragments. These types of algorithms can identify different pairs.

In this paper we apply MMP analysis to the enantioseparation data from our previous study, and find that, using this approach, many more statistically significant trends are found among the 19 CSPs than were obtained using conventional QSAR. We can see many small changes in structure that lead to very large differences in enantioseparation on specific CSPs. While the causes of these effects are not completely understood, we hope that providing this data may induce others to study this problem and experimental approach in further detail. To our knowledge, this is the first attempt to apply MMP methods to chromatographic enantioseparation data.

Results and Discussion

Definitions of alpha, Zmax, etc. are given in the Experimental Section. The number of clusters, the expected Zmax, and the number of statistically significant clusters is shown in Table 1. Interestingly, all four CSP_NOs that were statistically significant in cross-validation in our previous QSAR study have at least one statistically significant cluster, but 10 CSP_NOs for which we could not find any trend in our previous study have at least one statistically significant cluster. Especially interesting is CSP_NO 23735 (Chiralpak-AD), which afforded no statistically significant QSAR model in our previous study, but which has 65 statistically significant MMP clusters. The full list of Z, meanDiff, StdDiff, plus citations and SMILES for individual compounds in significant clusters, regardless of restrictions about citations, are provided in Supporting Information.

An example cluster is shown in Figure 1 (Cluster 1 from CSP_NO 23735). In this example, activity data for all 12 pairs was taken from a single publication by Yuan and coworkers [START_REF] Liao | Organocatalytic Asymmetric Conjugate Addition of 3-Monosubstituted Oxindoles to (E)-1,4-Diaryl-2-buten-1,4-diones: A Strategy for the Indirect Enantioselective Furanylation and Pyrrolylation of 3-Alkyloxindoles[END_REF]. Interestingly, this publication was focused on the synthesis of these compounds, and did not comment on the somewhat remarkable differences in chromatographic enantioselectivity.

Figure 2 shows Cluster 2 from CSP_NO 23735, a highly significant cluster with 10 pairs coming from seven different citations. Increased enantioselectivity for nitro vs. methyl ketone is observed across the entire series.

Table 2 lists a summary of statistically significant clusters where within each A-B pair the value of α comes from the same citation or the same set of authors, and the cluster contains at least two pairs. The citations are also listed in the table. The chemical change is summarized by the first pair in the cluster. Since the order of pairs in the cluster is arbitrary, the first pair may not have the largest change in enantioseparation. The other pairs have the same change in very similar molecules, as is seen in Figures 1 and2. Overall, a general tendency can be seen for MMPs with small chemical changes in structure, such as adding, changing, or moving a single atom, to give rise to large differences in enantioselectivity, up to 0.8-0.90 in log (α), or a factor of 6-8 in α. Clearly, MMPs showing such profound effects of structure on enantioselectivity can be helpful in understanding the mechanisms underlying the chromatographic separation of enantiomers. CSPs showing more unique molecule entries in ChirBase tend to show a greater number of significant clusters, a not unsurprising result given that with more molecules there is a greater chance of seeing something above noise. While a detailed investigation is beyond the scope of this study, several MMPs appearing in Table 2 that have a bearing on established theories for chiral chromatographic recognition will be pointed out. For the sake of brevity, the MMP clusters presented in the following discussion will be represented using a single pair from each cluster, however, the entire data set is available in the Supporting Information.

The chromatographic separation of enantiomers on CSPs relies upon the formation of transient diastereomeric adsorbates of differing free energy, with the more strongly adsorbed enantiomer spending proportionally more time on the column, and consequently eluting after the less strongly adsorbed enantiomer. Enantioseparation relies on a difference in the ability of the two enantiomers to undergo simultaneous bonding (or repulsive) interactions with the CSP. A variety of molecular interactions have been invoked to explain chiral molecular recognition on CSPs, with hydrogen bonding, π-π complexation of aromatic rings, ionic interactions and steric repulsion being the most common. Investigations into the mechanisms of chiral recognition on CSPs often examine the effect of systematic changes in chemical structure on chromatographic retention and enantioselectivity, with comparison between pairs or small families of closely related molecules sometimes being used to probe the mechanism of chromatographic chiral recognition for a given analyte-CSP combination.

For example, in Figure 1, replacement of the pyrrole nitrogen (NH) with a furan oxygen (O) leads to a marked decrease in enantioselectivity on the Chiralpak AD CSP, with a difference of 0.72 log(α) being observed in the most extreme case (α of 1.53 for the furan; α of 8.07 for the pyrrole). While a detailed investigation into the chiral recognition mechanisms for these molecules on this CSPs is beyond the scope of the present study, the extreme difference observed in this MMP cluster could suggest beneficial involvement of a pyrrole N-H hydrogen bond in chiral recognition with the chiral selector on the Chiralpak AD CSP. More subtle effects on chiral recognition for this series can also be observed. For example, m-F substitution on the benzyl aromatic group (b) enhances the enantioseparation gap between the pyrrole and furan, whereas m-Me (d), o-F or o-OMe substitution (structures d, c and f) leads to a sharp decrease the enantioseparation gap. Substitution in the para position of the aryl groups flanking the pyrrole or furan is seen to increase the enantioselectivity gap (g, h, I, k) while meta substitution decreases it (j, l).

Hypotheses about the involvement of an N-H hydrogen bond in chiral recognition can be also be tested by comparing enantioselectivity with the corresponding N-Me analog [START_REF] Welch | The Evolution of Chiral Stationary Phase Design in the Pirkle Laboratories[END_REF]. Four MMP clusters where NH methylation leads to a significant decrease in α are shown in Figure 3, suggesting an important beneficial role of the targeted N-H in chiral recognition in each of these cases.

Similarly, Figure 4 shows several MMP clusters that provide insight into the relative importance of hydrogen bonding of particular groups in chiral recognition on different CSPs. In Figure 4a, replacement of an ester O with an amide N-H leads to a strong increase in enantioselectivity, again suggesting involvement of the NH in chiral recognition. On the other hand, 4b and 4c show cases where the more strongly hydrogen bonding substituent leads to decreased enantioselectivity, a result that can arise from specifically increased adsorption of the less retained enantiomer or through generalized non-specific adsorption [START_REF] Pirkle | Effect of Superfluous Remote Polar Functionality on Chiral Recognition[END_REF].

Figure 4d shows an interesting case suggesting the importance of a lactone oxygen in chiral recognition while Figure 4e compares keto amide clusters to analogs where the ketone is replaced with an ester, leading to a sharp decrease in enantioselectivity. Conformational modeling (SI) reveals distinctly different conformations of the two analytes, with intramolecular CH-hydrogen bonding of the methyl ketone leading to a preferred conformation with anti-disposed carbonyl oxygens and the ester analog showing considerable conformational flexibility and a preference for syn oriented carbonyls.

Figure 4f compares clusters containing ureas and thioureas, showing much higher enantioselectivity in the former case. Again, the results are somewhat puzzling, but may be attributable to differences revealed by conformational modeling, with the thiourea showing a strong preference for an intramolecularly hydrogen bonded conformation where the aryl rings are also participating in π-π stacking (SI). enantioselectivity in the former case. Again, the results are somewhat puzzling, but may be attributable to differences revealed by conformational modeling, with the thiourea showing a strong preference for an intramolecularly hydrogen bonded conformation where the aryl rings are also participating in π-π stacking (SI). enantioselectivity in the former case. Again, the results are somewhat puzzling, but may be attributable to differences revealed by conformational modeling, with the thiourea showing a strong preference for an intramolecularly hydrogen bonded conformation where the aryl rings are also participating in π-π stacking (SI). 5a,b, although many cases where the opposite trend is observed are also known. The classic studies of Pirkle and co-workers [START_REF] Pirkle | A rational approach to the design of highly effective chiral stationary phases for the liquid chromatographic separation of enantiomers[END_REF][START_REF] Pirkle | Reciprocity in Chiral Recognition: Comparison of several chiral stationary phases[END_REF] provide detailed illustrations where, depending on orientation, increase in chain length can lead to dramatic improvement or decrease in enantioselectivity, or to situations where little change is observed. Figure 5c shows a cluster where change of a methyl to an isopropyl leads to enhanced enantioselectivity, a frequently observed trend toward improved enantioseparation with increased steric bulk at the stereocenter [START_REF] Allenmark | Chromatographic Enantioseparation[END_REF].

Figure 5 shows several MMP clusters in which change in the length or bulk of an alkyl substituent leads to a large difference in enantioselectivity. Examples where shorter chain lengths lead to improved enantioselectivity are shown in Figure 5a,b, although many cases where the opposite trend is observed are also known. The classic studies of Pirkle and co-workers [START_REF] Pirkle | A rational approach to the design of highly effective chiral stationary phases for the liquid chromatographic separation of enantiomers[END_REF][START_REF] Pirkle | Reciprocity in Chiral Recognition: Comparison of several chiral stationary phases[END_REF] provide detailed illustrations where, depending on orientation, increase in chain length can lead to dramatic improvement or decrease in enantioselectivity, or to situations where little change is observed. Figure 5c shows a cluster where change of a methyl to an isopropyl leads to enhanced enantioselectivity, a frequently observed trend toward improved enantioseparation with increased steric bulk at the stereocenter [START_REF] Allenmark | Chromatographic Enantioseparation[END_REF]. Figure 6 illustrates two MMP clusters showing improvement in separation of the enantiomers of carbinols as their acetyl derivatives. This general trend has been observed and commented on for a variety of acyl groups with the Whelko CSPs [START_REF] Leblanc | Enhanced Chromatographic Resolution of Alcohol Enantiomers as Phophate or Phosphonate Derivatives[END_REF], where the improvement in enantioselectivity is thought to be due to a mechanism-based advantage in the placement of a hydrogen bonding acceptor out of plane from the aromatic group. An analysis of MMPs for alcohol vs. the corresponding ester on the Whelko CSPs shows 39 instances of improved enantioselectivity for the acyl derivatives, with only two cases where the parent alcohol showed no improvement of enantioselectivity (Figure 6c). The same trend is observed for Chiralcel CA-1 with only three cases where the parent alcohol showed improved enantioselectivity. However, this trend is not universal for all the 19 CSPs.

Figure 7 shows a number of MMPs in which changes in aromatic substituents lead to large differences in enantioselectivity. Investigating aromatic substituent effects has long been used as a means to better understand chiral molecular recognition, particularly for the π-acidic and π-basic CSPs where π-π interaction is believed to be important. Hammet-type analysis has often been used to gain an appreciation of the relative contribution of these electronic effects, although the results are often only roughly directional in nature [START_REF] Okamoto | Polysaccharide derivatives for chromatographic separation of enantiomers[END_REF][START_REF] Dai | Enantiomeric Recognition of Racemic 4-Aryl-1,4-dihydropyridine Derivatives via Chiralpak AD-H Stationary Phases[END_REF][START_REF]Chiral recognition by enantioselective liquid chromatography: Mechanisms and modern chiral stationary phases, Michael Lämmerhofer[END_REF][START_REF] Toshihiko | Quantitative in Silico Chromatography: Computational Modelling of Molecular Interactions[END_REF].

Figure 8 shows several MMP clusters where the effects of ortho aromatic substitution on chiral recognition can be observed. Ortho substitution of aromatic rings often leads to unusual and unexpected results in chromatographic enantioseparation, when compared with substitution at the para or meta positions [START_REF] Welch | The Evolution of Chiral Stationary Phase Design in the Pirkle Laboratories[END_REF][START_REF] Pirkle | A rational approach to the design of highly effective chiral stationary phases for the liquid chromatographic separation of enantiomers[END_REF]. This behavior is thought to be caused by disruption of molecular conformation caused by steric effects of the ortho substituent. In Cluster a, ortho substitution dramatically decreases enantioselectivity, whereas in clusters b and c, ortho substitution improves enantioselectivity. Figure 6 illustrates two MMP clusters showing improvement in separation of the enantiomers of carbinols as their acetyl derivatives. This general trend has been observed and commented on for a variety of acyl groups with the Whelko CSPs [START_REF] Leblanc | Enhanced Chromatographic Resolution of Alcohol Enantiomers as Phophate or Phosphonate Derivatives[END_REF], where the improvement in enantioselectivity is thought to be due to a mechanism-based advantage in the placement of a hydrogen bonding acceptor out of plane from the aromatic group. An analysis of MMPs for alcohol vs. the corresponding ester on the Whelko CSPs shows 39 instances of improved enantioselectivity for the acyl derivatives, with only two cases where the parent alcohol showed no improvement of enantioselectivity (Figure 6c). The same trend is observed for Chiralcel CA-1 with only three cases where the parent alcohol showed improved enantioselectivity. However, this trend is not universal for all the 19 CSPs.

Figure 7 shows a number of MMPs in which changes in aromatic substituents lead to large differences in enantioselectivity. Investigating aromatic substituent effects has long been used as a means to better understand chiral molecular recognition, particularly for the π-acidic and π-basic CSPs where π-π interaction is believed to be important. Hammet-type analysis has often been used to gain an appreciation of the relative contribution of these electronic effects, although the results are often only roughly directional in nature [START_REF] Okamoto | Polysaccharide derivatives for chromatographic separation of enantiomers[END_REF][START_REF] Dai | Enantiomeric Recognition of Racemic 4-Aryl-1,4-dihydropyridine Derivatives via Chiralpak AD-H Stationary Phases[END_REF][START_REF]Chiral recognition by enantioselective liquid chromatography: Mechanisms and modern chiral stationary phases, Michael Lämmerhofer[END_REF][START_REF] Toshihiko | Quantitative in Silico Chromatography: Computational Modelling of Molecular Interactions[END_REF].

Figure 8 shows several MMP clusters where the effects of ortho aromatic substitution on chiral recognition can be observed. Ortho substitution of aromatic rings often leads to unusual and unexpected results in chromatographic enantioseparation, when compared with substitution at the para or meta positions [START_REF] Welch | The Evolution of Chiral Stationary Phase Design in the Pirkle Laboratories[END_REF][START_REF] Pirkle | A rational approach to the design of highly effective chiral stationary phases for the liquid chromatographic separation of enantiomers[END_REF]. This behavior is thought to be caused by disruption of molecular conformation caused by steric effects of the ortho substituent. In Cluster a, ortho substitution dramatically decreases enantioselectivity, whereas in clusters b and c, ortho substitution improves enantioselectivity. While some MMPs turned up in the study help to corroborate previous notions about chromatographic enantioseparation, others are unusual, strange, or in some way difficult to interpret. Surfacing of these new relationships may be one of the most valuable aspects of the MMP data mining approach. Several examples of puzzling MMP clusters are presented in Figure 9. For example, the hydrazine and the olefin shown in 9b are markedly different in terms of structure, electronic properties, ability to participate in hydrogen bonding and bond rotatability, but the very fact that the MMP search brings this pair to our attention forces us to consider certain aspects of chiral recognition that would otherwise escape our notice. It is important to point out that caution must be exercised when interpreting differences between MMPs, as a simple atom or group differences can often lead to multiple effects. For example, change of an amide NH to N-Me not only eliminates a hydrogen bond donor site, but also leads to increased electron density on the amide carbonyl oxygen and often to a shifting in the population of amide rotors. Similarly, introduction of a methoxy substituent on an aromatic ring leads to increased π-basicity, but also introduces a group that can participate in hydrogen bonding or steric interactions. Finally, in this study we have discussed MMP changes in terms of particular analyte-CSP interactions, but in While some MMPs turned up in the study help to corroborate previous notions about chromatographic enantioseparation, others are unusual, strange, or in some way difficult to interpret. Surfacing of these new relationships may be one of the most valuable aspects of the MMP data mining approach. Several examples of puzzling MMP clusters are presented in Figure 9. For example, the hydrazine and the olefin shown in 9b are markedly different in terms of structure, electronic properties, ability to participate in hydrogen bonding and bond rotatability, but the very fact that the MMP search brings this pair to our attention forces us to consider certain aspects of chiral recognition that would otherwise escape our notice. While some MMPs turned up in the study help to corroborate previous notions about chromatographic enantioseparation, others are unusual, strange, or in some way difficult to interpret. Surfacing of these new relationships may be one of the most valuable aspects of the MMP data mining approach. Several examples of puzzling MMP clusters are presented in Figure 9. For example, the hydrazine and the olefin shown in 9b are markedly different in terms of structure, electronic properties, ability to participate in hydrogen bonding and bond rotatability, but the very fact that the MMP search brings this pair to our attention forces us to consider certain aspects of chiral recognition that would otherwise escape our notice. It is important to point out that caution must be exercised when interpreting differences between MMPs, as a simple atom or group differences can often lead to multiple effects. For example, change of an amide NH to N-Me not only eliminates a hydrogen bond donor site, but also leads to increased electron density on the amide carbonyl oxygen and often to a shifting in the population of amide rotors. Similarly, introduction of a methoxy substituent on an aromatic ring leads to increased π-basicity, but also introduces a group that can participate in hydrogen bonding or steric interactions. Finally, in this study we have discussed MMP changes in terms of particular analyte-CSP interactions, but in It is important to point out that caution must be exercised when interpreting differences between MMPs, as a simple atom or group differences can often lead to multiple effects. For example, change of an amide NH to N-Me not only eliminates a hydrogen bond donor site, but also leads to increased electron density on the amide carbonyl oxygen and often to a shifting in the population of amide rotors.

Similarly, introduction of a methoxy substituent on an aromatic ring leads to increased π-basicity, but also introduces a group that can participate in hydrogen bonding or steric interactions. Finally, in this study we have discussed MMP changes in terms of particular analyte-CSP interactions, but in some cases, more general effects could be at play. For example, some MMPs may reflect a general tendency for improved enantioselectivity on any CSP, simply reflecting a molecular change that makes a subtly chiral molecule more easily discriminated by a variety of different CSPs. An extreme example would be the MMP comparing a compound containing a 'chiral methylene' (H and D substituents on the stereocenter-almost impossible to resolve chromatographically) with another molecule where D is replaced with almost anything (N, O, Me, etc. generally easily resolved chromatographically).

Experimental Section

Source of Data

Beginning in the late 1980s, Roussel and co-workers began to develop ChirBase, a database of chiral HPLC separations. The database, which now comprises more than 200,000 entries for over 1800 CSPs, is widely used as a tool for analytical and preparative chromatographic method development. The data included in ChirBase are extracted from the printed and online scientific literature. Each ChirBase record contains different categories of information related to sample, CSP, bibliographic reference, experimental conditions, retention and separation data. Since ChirBase can report only what is reported in the literature, some information may be missing for any given separation record.

Definition of Enantioselectivity

Several metrics for distinguishing the best enantioseparation entry are possible, with chromatographic resolution (Rs) being the most practical term that describes how well two peaks are resolved. However, resolution values are only rarely reported in non-chromatographic literature. Alternatively, the chromatographic separation factor (α) is a useful proxy value that can be a good indicator of the quality of separation [START_REF] Gong | Estimating Chromatographic Enantioselectivity (α) from Gradient Chiral Chromatography Data[END_REF], and this is the measure selected for this study. The term α is equal to the ratio of the retention factors for the two enantiomers on the CSP (k 2 /k 1 ) and is also given by ((t 2t 0 )/t 0 )/((t 1t 0 )/t 0 ) where t 1 and t 2 are the retention times for the first and second eluted enantiomers, and where t 0 is the retention time for an unretained solute (the so called 'void time' [START_REF] Pirkle | A Convenient Void Volume Marker for Several Chiral HPLC Columns[END_REF]). While many Chirbase entries fail to report α, many entries report retention times for the two enantiomers, column dimensions, and mobile phase flow rate, from which an estimate of t 0 can be made, thereby allowing an estimate of α [START_REF] Sheridan | Toward structure-based predictive tools for the selection of chiral stationary phases for the chromatographic separation of enantiomers[END_REF]. Of course, α is to some degree dependent upon temperature, with some enantioseparations being improved at sub-ambient temperatures and others being improved at elevated temperatures. These changes in enantioselectivity are generally small in the range within which chromatographic experiments are typically carried out. While most of the separations in Chirbase are carried out near room temperature, not all reported separations in the database contain an explicit description of temperature, and for the purposes of these studies, the effect of temperature is considered as a second order perturbation, and not explicitly factored into the modeling approach.

Selection of Data to Model

In our previous work we needed to filter the separation records in Chirbase. Data used in that earlier study and this one required that there be a clearly defined structure for the molecule being separated, that there be a single stereogenic center, and that the reported value of α be within a reasonable range [START_REF] Allenmark | Chromatographic Enantioseparation[END_REF][START_REF] Pirkle | Considerations of chiral recognition relevant to the liquid chromatography separation of enantiomers[END_REF][START_REF] Okamoto | Polysaccharide derivatives for chromatographic separation of enantiomers[END_REF][START_REF] Armstrong | Enantiomeric separations in chromatography[END_REF][START_REF] Francotte | Enantioselective chromatography. An essential and versatile tool for the analytical and preparative separation of enantiomers[END_REF][START_REF] Allenmark | Chromatography on chiral stationary phases[END_REF][START_REF] Lammerhofer | Liquid chromatographic enantiomer separation and chiral recognition by cinchona alkaloid-derived enantioselective separation materials[END_REF][START_REF] Suzuki | Quantitative structure-enantioselective retention relationships for chromatographic separation of arylalkylcarbinols on Pirkle type chiral stationary phases[END_REF][START_REF] Carotti | LFER and CoMFA studies on optical resolution of -alkyl -aryloxyacetic acid methyl esters on DACH-DNB chiral stationary phase[END_REF][START_REF] Fabian | Modeling the Chromatographic Enantioseparation of Aryl-and Hetarylcarbinols on ULMO, a Brush-Type Chiral Stationary Phase, by 3D-QSAR Techniques[END_REF][START_REF] Del Rio | Enantiophore Modeling in 3D-QSAR. A Data Mining Application on Whelk-O1 Chiral Stationary Phase[END_REF][START_REF] Sheridan | Toward structure-based predictive tools for the selection of chiral stationary phases for the chromatographic separation of enantiomers[END_REF].

In ChirBase at least some data is missing for most determinations of α, and the only data guaranteed to be present is the structure of the molecule being separated and the stationary phase, represented as the CSP_NO, which is an index specific to ChirBase. ChirBase may represent two stationary phases of opposite chirality, e.g., CrownPak-CR(+) and CrownPak-CR(-), as a single CSP_NO since both should separate enantiomers of a given racemic molecule in an identical manner. Our approach was to build a model predicting α for individual CSP_NO. For that, one must generate a set of unique molecules tested on that CSP_NO and their values of α. Chirbase contains a number of determinations for each unique molecule on a CSP_NO under a variety of "conditions" (temperature, mobile phase, etc.), but as noted above, only some conditions are noted for each record, and even if all the conditions were recorded, there would not be enough data to make a model for each condition. Therefore for our QSAR study we needed to model each unique molecule under "the best recorded condition" for that CSP_NO. The α we use as an activity for a molecule is the 95 th percentile value of α over all occurrences of that molecule on the specific CSP_NO. In that way the activity represents the "best recorded condition" for that molecule and CSP_NO while eliminating extreme outliers.

There are ~1800 unique CSP_NOs in the ChirBase, and it would not make sense to model all of them. For most CSP_NOs, not enough unique molecules have been tested to make a generalizable QSAR model. Also, for most CSP_NOs, there is not enough variation in α to make modeling possible, i.e., if there are no examples of both poorly-separated and well-separated molecules, there is no possibility of modeling the ease of separation. Therefore, in our previous paper we focused on 19 CSP_NOs that had ≥100 unique molecules and a standard deviation in α ≥ 1.5. In this study we look at the same CSP_NOs as in the previous study, so a comparison between conventional QSAR and MMP analysis can be made.

Transformation of Data

In QSAR, one may transform the activity (the property being measured or estimated) to obtain a more self-consistent model. In our previous paper we tried α and log(α), the base-10 log. The cross-validated R 2 for log(α) was almost always better than α. In retrospect this is not surprising since α is a ratio and empirically, QSAR models using the log of the ratio instead of the ratio itself generally perform better. We will use log(α) as the activity in our MMP analysis. For example log(α) = 0 represents an α value of 1, i.e., no enantioseparation, and log(α) = 1 represents an α value of 10, a very high degree of separation. Differences in activity will also be in units of log(α).

Method for Matched Molecular Pairs

We will use the T-ANALYZE [START_REF] Sheridan | Molecular transformation as a way of finding and exploiting consistent local QSAR[END_REF] method developed in our laboratory. It uses a maximum common substructure method to find the matched pairs. That approach is useful for finding changes within rings, of which we have several examples. Imagine a dataset of molecules with an activity assigned to each. Steps are the following:

1.

Pairs of molecules A and B are selected such that A and B are topologically similar (similarity > 0.65 using the atom-pair descriptor and the Dice similarity index). This step is useful because finding maximum common substructure (MCS) is slow and the selection reduces the number of pairs that must be examined by MCS.

2.

A MCS is defined for each pair A-B. The change in structure A → B is defined by the RECS (remainder after elimination of common substructure), which is the set of atoms that remain in A and B after the atoms in the MCS are eliminated. One can think of A → B as the "change in structure". Some A → B changes may be discarded if the changes occur in more than one place on the molecule.

3.

Sets of A → B pairs are clustered such that all pairs in each cluster have the same type of chemical change, all the pairs are distinct, and all the molecules in the cluster are topologically similar. That is, each pair in the cluster contains the same change in the same molecular context. Here we will use Npair to be the number of pairs in a cluster. Some clusters may contain a single pair. Nclusters will represent the number of clusters from a particular dataset.So far, the analysis has ignored activity. Further steps take the activity into account.

4.

Each molecule in the cluster is assigned an activity. Consider a cluster of three pairs A1 → B1, A2 → B2, A3 → B3. One can calculate the difference in activity between A1 and B1, A2 and B2, etc. The mean difference in activity for the cluster is called meanDiff and the standard deviation is called StdDiff. If the absolute value of meanDiff is large and StdDiff is small, the same chemical change is producing an equivalent change in activity. A metric Z compares meanDiff of the cluster with Npair pairs with the meanDiff expected by assembling Npair randomly selected pairs not necessarily in the same cluster: Z = (meanDiff(real) -meanDiff(random))/StdDiff(random,Npairs)

For randomly selected pairs, the changes in activity will tend to cancel out, so the expected meanDiff(random) is close to zero and StdDiff will have a value that depends on Npairs. As a consequence, as Npairs gets larger, the StdDiff(random,Npairs) becomes smaller.We use Z is to score clusters in a dataset by statistical significance. A cluster with a smaller absolute meanDiff can have a higher absolute Z than a cluster with a larger absolute meanDiff if there are more pairs in the first cluster.

5.

By convention, we want the first molecule in the pair to have the higher activity, so we may reassign molecules in all pairs in the cluster as A and B so that meanDiff and Z is positive. 6.

Clusters can be written in order of decreasing Z, decreasing meanDiff, or decreasing Npairs.

The default of T-ANALYZE is to sort by decreasing Z.

Selection of Statistically Significant Clusters

The term, Z, provides a measure of the statistical significance of a single cluster compared to a cluster composed of randomly selected pairs. This term helps to identify the truly important clusters with a high level of statistical significance and ignore those clusters that may reflect nothing more than chance or statistical fluctuation. One aspect that was not considered in the original formulation of T-ANALYZE is what value of Z one would consider statistically significant given that a very large number of clusters can be produced from a given dataset. If Nclusters is large, it is possible for a high Z to occur by chance even if there is no real consistency between chemical changes and activity changes in the dataset.

We approach this issue by simulating assembling a set of Nclusters clusters. Given that any Z is already normalized by Npairs, we need no longer pay attention to the number of pairs in each cluster. To simulate the Z of a cluster, pick a random number from a Gaussian distribution with a standard deviation of 1 and mean of 0 and take its absolute value. (The absolute value is taken since all Z > 0 by convention in step 5 above). This value for Z represents one simulated cluster. For arbitrary values of Ncluster, generate Ncluster simulated clusters and note the maximum simulated Z over all the clusters, which we will call maxZ. Do this, say, 1000 times. One can calculate a mean and standard deviation for maxZ over these 1000 trials. Only five percent of the time we would expect maxZ to be above mean(maxZ) + 2 standard deviations(maxZ), so any real Z in a group of Nclusters clusters that is above this value is "real" with 95% confidence. That is, in only one out of 20 times will that dataset produce that large a Zmax by chance. We can repeat the above analysis for many values of Nclusters. Figure 10 shows maxZ+2 standard deviations as a function of log(Nclusters). Clearly there is a linear relationship such that we can predict maxZ for any value of Nclusters:

Expected maxZ (95% percentile) = 2.42 + 0.543 log(Nclusters).

(

) 2
For a dataset with Nclusters clusters, we need consider only those clusters where Z of the cluster exceeds the value in the above equation, as these will be "statistically significant." Another possible criterion for considering a cluster interesting is that it contain at least two pairs. Clusters containing one pair may be statistically significant, but it is better to have confirmation that a particular chemical change produced a change in enantioseparation in the same direction.

Conclusions

To our knowledge, this is the first time that MMP analysis has been carried out to study chromatographic enantioseparation. We have only begun to investigate this topic, and there are no doubt many more interesting clusters in ChirBase. Here, we confined our analysis to 19 CSP_NOs that were thought to have the best chance of showing activity trends by descriptor-based QSAR methods. However, these results strongly suggest that many other CSP_NOs might have statistically significant clusters by MMP even though they show no trend by QSAR. Also, we did not discuss significant clusters in which the α values for A and B were from citations from different authors. In some of these cases (e.g. in Figure 2) we did see very significant clusters and some of the trends might be real.

Finally it is possible that alternative methods for identifying matched pairs would identify pairs that our maximum common substructure algorithm would not. The fact that small changes in structure gives rise to large changes in enantioseparation in a large variety of CSPs is consistent with the idea that enantiomers make very specific contacts with the CSP. This also might be an explanation as to why making conventional QSAR models of enantioseparation is so difficult, as it is generally accepted that a rugged activity landscape makes finding QSAR difficult.

Supplementary Materials: Supplementary materials can be accessed at: http://www.mdpi.com/1420-3049/21/ 10/1297/s1.

Acknowledgments: This work is dedicated to Stig Allenmark on the occasion of his 80th birthday-friend, collaborator and vital force in the development of the science of chromatographic enantioseparation. We are grateful to the Department of Process & Analytical Chemistry for providing financial support for this collaboration.

Author Contributions: R.P.S., C.J.W. and P.P. conceived and designed the experiments and wrote the paper; E.C.S., C.R., and W.H.P. assisted with the analysis and interpretation of the results.

Conflicts of Interest:

The authors declare no conflict of interest.

Selection of Clusters

There may be criteria other than statistical significance that we may wish to consider to determine which clusters are interesting. Given that determinations of compounds in a given cluster may be from different labs with the enantioseparation run under different conditions, it is possible that the difference in α between A and B can reflect differences between labs, rather than real differences between molecules. Therefore we might suggest that the values of α in the entire cluster need to be taken from the same citation, or at least from citations with a common set of authors (presumably with the experiment run in the same lab). A weaker version of this might be to require only that the α values of A and B within each pair be from the same authors, the assumption being that what matters is the difference between A and B within each pair, not the difference between pairs. Another possible criterion for considering a cluster interesting is that it contain at least two pairs. Clusters containing one pair may be statistically significant, but it is better to have confirmation that a particular chemical change produced a change in enantioseparation in the same direction.

Conclusions

To our knowledge, this is the first time that MMP analysis has been carried out to study chromatographic enantioseparation. We have only begun to investigate this topic, and there are no doubt many more interesting clusters in ChirBase. Here, we confined our analysis to 19 CSP_NOs that were thought to have the best chance of showing activity trends by descriptor-based QSAR methods. However, these results strongly suggest that many other CSP_NOs might have statistically significant clusters by MMP even though they show no trend by QSAR. Also, we did not discuss significant clusters in which the α values for A and B were from citations from different authors. In some of these cases (e.g. in Figure 2) we did see very significant clusters and some of the trends might be real.

Finally it is possible that alternative methods for identifying matched pairs would identify pairs that our maximum common substructure algorithm would not. The fact that small changes in structure gives rise to large changes in enantioseparation in a large variety of CSPs is consistent with the idea that enantiomers make very specific contacts with the CSP. This also might be an explanation as to why making conventional QSAR models of enantioseparation is so difficult, as it is generally accepted that a rugged activity landscape makes finding QSAR difficult.

Figure 1 .Figure 1 .

 11 Figure 1. Expanded view of Cluster 1 for CSP_NO 23735 (Chiralpak-AD). There are 12 pairs (a-l) in this cluster. Differences in the structure are highlighted in red. Next to each compound is shown the Chirbase compound number and log(α). The meanDiff within each pair is written between the molecules. This is an example of a cluster where all the values of α come from the same reference.

Figure 2 .

 2 Figure 2. Expanded view of Cluster 2 for CSP_NO 23735 (Chiralpak-AD). There are 10 pairs in this cluster. Differences in structure are highlighted in red. Next to each compound is shown the Chirbase compound number and log(α). The meanDiff within each pair is written between the molecules. This is an example of a cluster were the values of α can come from different references. See supporting information for conformational modeling showing differences in preferred conformations of nitro and methyl ketone analogs.

Figure 2 .

 2 Figure 2. Expanded view of Cluster 2 for CSP_NO 23735 (Chiralpak-AD). There are 10 pairs in this cluster. Differences in structure are highlighted in red. Next to each compound is shown the Chirbase compound number and log(α). The meanDiff within each pair is written between the molecules. This is an example of a cluster were the values of α can come from different references. See supporting information for conformational modeling showing differences in preferred conformations of nitro and methyl ketone analogs.

Figure 3 .

 3 Figure 3. Matched Molecular Pairs illustrating the effects of N-H methylation on several analyte-CSP combinations.

Figure 4 .Figure 3 .

 43 Figure 4. Matched Molecular Pairs illustrating effects of hydrogen bonding on chiral recognition.

Figure 3 .

 3 Figure 3. Matched Molecular Pairs illustrating the effects of N-H methylation on several analyte-CSP combinations.

Figure 4 .Figure 4 .

 44 Figure 4. Matched Molecular Pairs illustrating effects of hydrogen bonding on chiral recognition.

Figure 5

 5 Figure5shows several MMP clusters in which change in the length or bulk of an alkyl substituent leads to a large difference in enantioselectivity. Examples where shorter chain lengths lead to improved enantioselectivity are shown in Figure5a,b, although many cases where the opposite trend is observed are also known. The classic studies of Pirkle and co-workers[START_REF] Pirkle | A rational approach to the design of highly effective chiral stationary phases for the liquid chromatographic separation of enantiomers[END_REF][START_REF] Pirkle | Reciprocity in Chiral Recognition: Comparison of several chiral stationary phases[END_REF] provide detailed illustrations where, depending on orientation, increase in chain length can lead to dramatic improvement or decrease in enantioselectivity, or to situations where little change is observed. Figure5cshows a cluster where change of a methyl to an isopropyl leads to enhanced enantioselectivity, a frequently observed trend toward improved enantioseparation with increased steric bulk at the stereocenter[START_REF] Allenmark | Chromatographic Enantioseparation[END_REF].

Figure 5 .

 5 Figure 5. Matched Molecular Pairs illustrating effects of chain length and steric bulk on chiral recognition.

Figure 5 .

 5 Figure 5. Matched Molecular Pairs illustrating effects of chain length and steric bulk on chiral recognition.

Figure 6 .

 6 Figure 6. Matched Molecular Pairs illustrating effects of ester vs. alcohol on chiral recognition. (a) MMP from Whelk-O CSP (b) MMP from Chiralcel CA CSP (c) Distribution of MMPs illustrating effects of ester vs. alcohol on chiral recognition on Whelk-O CSPs. MMPs are ranked according to difference in log(α).

Figure 7 .

 7 Figure 7. Matched Molecular Pairs illustrating effects of aromatic substituents on chiral recognition.

Whelko Z = 4 .Figure 6 .

 46 Figure 6. Matched Molecular Pairs illustrating effects of ester vs. alcohol on chiral recognition. (a) MMP from Whelk-O CSP (b) MMP from Chiralcel CA CSP (c) Distribution of MMPs illustrating effects of ester vs. alcohol on chiral recognition on Whelk-O CSPs. MMPs are ranked according to difference in log(α).

Figure 6 .

 6 Figure 6. Matched Molecular Pairs illustrating effects of ester vs. alcohol on chiral recognition. (a) MMP from Whelk-O CSP (b) MMP from Chiralcel CA CSP (c) Distribution of MMPs illustrating effects of ester vs. alcohol on chiral recognition on Whelk-O CSPs. MMPs are ranked according to difference in log(α).

Figure 7 .

 7 Figure 7. Matched Molecular Pairs illustrating effects of aromatic substituents on chiral recognition.

Whelko Z = 4 .Figure 7 .

 47 Figure 7. Matched Molecular Pairs illustrating effects of aromatic substituents on chiral recognition.

Figure 8 .

 8 Figure 8. Matched Molecular Pairs illustrating the effects of ortho substitution on chiral recognition.

Figure 9 .

 9 Figure 9. Matched Molecular Pairs illustrating difficult to categorize effects that would normally escape notice.

Figure 8 .

 8 Figure 8. Matched Molecular Pairs illustrating the effects of ortho substitution on chiral recognition.

Figure 8 .

 8 Figure 8. Matched Molecular Pairs illustrating the effects of ortho substitution on chiral recognition.

Figure 9 .

 9 Figure 9. Matched Molecular Pairs illustrating difficult to categorize effects that would normally escape notice.

Figure 9 .

 9 Figure 9. Matched Molecular Pairs illustrating difficult to categorize effects that would normally escape notice.

Figure 10 .

 10 Figure 10. Relationship between the statistically significant level of maxZ and the number of clusters.

Figure 10 .

 10 Figure 10. Relationship between the statistically significant level of maxZ and the number of clusters.

Table 2 .

 2 Clusters where M > 1 and the activities for A and B come from the same reference.

	Molecules 2016, 21, 1297							6 of 27
	CSP_NO	Cluster	Z	No. Pairs	Mean (Diff)	Std (Diff)	Example	Reference
	23735 Chiralpak-AD	1	11.52	12	0.43	0.23

Table 2 .

 2 Clusters where M > 1 and the activities for A and B come from the same reference.

	Molecules 2016, 21, 1297							6 of 27
	CSP_NO	Cluster	Z	No. Pairs Mean (Diff)	Std (Diff)	Example	Reference
	23735 Chiralpak-AD		1	11.52	12	0.43	0.23	[15] [15]
		5	5	6.93 6.93	6 6	0.35 0.35	0.03 0.03	[16]
			27	5.62	2	0.49	0.24	[17]
			28	5.61	2	0.49	0.27	[17]
			35	5.47	2	0.48	0.08	[18,19]
			44	5.14	2	0.46	0.07	[18,19]

Table 2 .

 2 Clusters where M > 1 and the activities for A and B come from the same reference.

	CSP_NO	Cluster	Z	No. Pairs Mean (Diff)	Std (Diff)	Example	Reference
	23735 Chiralpak-AD		1	11.52	12	0.43	0.23	[15]
			5	6.93	6	0.35	0.03	[16] [16]
		27	27	5.62 5.62	2 2	0.49 0.49	0.24 0.24	[17]
			28	5.61	2	0.49	0.27	[17]
			35	5.47	2	0.48	0.08	[18,19]
			44	5.14	2	0.46	0.07	[18,19]

Molecules 2016, 21, 1297 6 of 27

Table 2 .

 2 Clusters where M > 1 and the activities for A and B come from the same reference.

	CSP_NO	Cluster	Z	No. Pairs Mean (Diff)	Std (Diff)	Example	Reference
	23735 Chiralpak-AD		1	11.52	12	0.43	0.23	[15]
			5	6.93	6	0.35	0.03	[16]
			27	5.62	2	0.49	0.24	[17] [17]
		28	28	5.61 5.61	2 2	0.49 0.49	0.27 0.27	[17]
			35	5.47	2	0.48	0.08	[18,19]
			44	5.14	2	0.46	0.07	[18,19]

Molecules 2016, 21, 1297 6 of 27

Table 2 .

 2 Clusters where M > 1 and the activities for A and B come from the same reference.

	Molecules 2016, 21, 1297							6 of 27
	CSP_NO	Cluster	Z	No. Pairs Mean (Diff)	Std (Diff)	Example	Reference
	23735 Chiralpak-AD		1	11.52	12	0.43	0.23	[15]
			5	6.93	6	0.35	0.03	[16]
			27	5.62	2	0.49	0.24	[17]
			28	5.61	2	0.49	0.27	[17] [17]
		35	35	5.47 5.47	2 2	0.48 0.48	0.08 0.08	[18,19]
			44	5.14	2	0.46	0.07	[18,19]

Table 2 .

 2 Clusters where M > 1 and the activities for A and B come from the same reference.

	CSP_NO	Cluster	Z	No. Pairs Mean (Diff)	Std (Diff)	Example	Reference
	23735 Chiralpak-AD	1	11.52	12	0.43	0.23		[15]
		5	6.93	6	0.35	0.03		[16]
		27	5.62	2	0.49	0.24		[17]
		28	5.61	2	0.49	0.27		[17]
		35	5.47	2	0.48	0.08		[18,19] [18,19]
		44	5.14	2	0.46	0.07		[18,19]

Table 2 .

 2 Cont.

			28	5.61	2	0.49	0.27		[17]
			35	5.47	2	0.48	0.08		[18,19]
	CSP_NO	Cluster	Z	No. Pairs	Mean (Diff)	Std (Diff)	Example	Reference
		44	44	5.14 5.14	2 2	0.46 0.46	0.07 0.07		[18,19] [18,19]
	Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297								7 of 27 7 of 27 7 of 27 7 of 27 7 of 27
		45	45 45 45 45 45	5.10 5.10 5.10 5.10 5.10 5.10	5 5 5 5 5 5	0.29 0.29 0.29 0.29 0.29 0.29	0.24 0.24 0.24 0.24 0.24 0.24		[20] [20] [20] [20] [20] [20]
		46	46 46 46 46 46	5.08 5.08 5.08 5.08 5.08 5.08	2 2 2 2 2 2	0.45 0.45 0.45 0.45 0.45 0.45	0.24 0.24 0.24 0.24 0.24 0.24		[19,21] [19,21] [19,21] [19,21] [19,21] [19,21]
		47	47 47 47 47 47	5.05 5.05 5.05 5.05 5.05 5.05	2 2 2 2 2 2	0.45 0.45 0.45 0.45 0.45 0.45	0.19 0.19 0.19 0.19 0.19 0.19		[22] [22] [22] [22] [22] [22]
		61	61 61 61 61 61	4.80 4.80 4.80 4.80 4.80 4.80	4 4 4 4 4 4	0.31 0.31 0.31 0.31 0.31 0.31	0.01 0.01 0.01 0.01 0.01 0.01		[23] [23] [23] [23] [23] [23]
		65	65 65 65 65 65	4.78 4.78 4.78 4.78 4.78 4.78	4 4 4 4 4 4	0.31 0.31 0.31 0.31 0.31 0.31	0.16 0.16 0.16 0.16 0.16 0.16		[24,25] [24,25] [24,25] [24,25] [24,25] [24,25]
	45167 Chiralpak-AS 45167 Chiralpak-AS 45167 Chiralpak-AS 45167 Chiralpak-AS 45167 Chiralpak-AS		1 1 1 1 1	7.84 7.84 7.84 7.84 7.84	2 2 2 2 2	0.76 0.76 0.76 0.76 0.76	0.07 0.07 0.07 0.07 0.07		[26-28] [26-28] [26-28] [26-28] [26-28]
			28 28 28 28 28	4.57 4.57 4.57 4.57 4.57	2 2 2 2 2	0.44 0.44 0.44 0.44 0.44	0.29 0.29 0.29 0.29 0.29		[29] [29] [29] [29] [29]

Table 2 . Cont. CSP_NO Cluster Z No. Pairs Mean (Diff) Std (Diff) Example Reference

 2

		61	4.80	4	0.31	0.01
	45167 Chiralpak-AS	1	7.84	2	0.76	0.07

Table 2 . Cont. CSP_NO Cluster Z No. Pairs Mean (Diff) Std (Diff) Example Reference

 2

	3	8.05	2	0.86	0.08
	6	7.00	4	0.52	0.09

Table 2 . Cont. CSP_NO Cluster Z No. Pairs Mean (Diff) Std (Diff) Example Reference

 2

	15	6.17	3	0.51	0.09
	17	5.85	2	0.62	0.06

Table 2 .

 2 Cont.

	Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297								10 of 27 10 of 27 10 of 27 10 of 27
			23 23 23 23	5.61 5.61 5.61 5.61	2 2 2 2	0.60 0.60 0.60 0.60	0.26 0.26 0.26 0.26		[37] [37] [37] [37]
	CSP_NO	Cluster 25 25 25 25	Z 5.49 5.49 5.49 5.49	No. Pairs 2 2 2 2	Mean (Diff) 0.58 0.58 0.58 0.58	Std (Diff) 0.46 0.46 0.46 0.46	Example	Reference [30,32] [30,32] [30,32] [30,32]
		27	27 27 27 27	5.43 5.43 5.43 5.43 5.43	2 2 2 2 2	0.57 0.57 0.57 0.57 0.57	0.01 0.01 0.01 0.01 0.01		[30,32] [30,32] [30,32] [30,32] [30,32]
		28	28 28 28 28	5.43 5.43 5.43 5.43 5.43	2 2 2 2 2	0.58 0.58 0.58 0.58 0.58	0.06 0.06 0.06 0.06 0.06		[30] [30] [30] [30] [30]
		33	33 33 33 33	5.11 5.11 5.11 5.11 5.11	2 2 2 2 2	0.55 0.55 0.55 0.55 0.55	0.01 0.01 0.01 0.01 0.01		[30] [30] [30] [30] [30]
		35	35 35 35 35	5.10 5.10 5.10 5.10 5.10	2 2 2 2 2	0.54 0.54 0.54 0.54 0.54	0.03 0.03 0.03 0.03 0.03		[30,32] [30,32] [30,32] [30,32] [30,32]
	Molecules 2016, 21, 1297								11 of 27
		37	37	5.07 5.07	2 2	0.54 0.54	0.31 0.31		[37] [37]
			44	4.95	3	0.41	0.08		[38-40]
			47	4.82	2	0.52	0.01		[30]

Table 2 .

 2 Cont.

	Molecules 2016, 21, 1297								11 of 27
	Molecules 2016, 21, 1297 Molecules 2016, 21, 1297								11 of 27 11 of 27
	CSP_NO	Cluster 37 37 37 37 37 37	Z 5.07 5.07 5.07 5.07 5.07 5.07	No. Pairs 2 2 2 2 2 2	Mean (Diff) 0.54 0.54 0.54 0.54 0.54 0.54	Std (Diff) 0.31 0.31 0.31 0.31 0.31 0.31	Example	Reference [37] [37] [37] [37] [37] [37]
		44	44 44 44 44 44 44	4.95 4.95 4.95 4.95 4.95 4.95 4.95	3 3 3 3 3 3 3	0.41 0.41 0.41 0.41 0.41 0.41 0.41	0.08 0.08 0.08 0.08 0.08 0.08 0.08		[38-40] [38-40] [38-40] [38-40] [38-40] [38-40] [38-40]
		47	47 47 47 47 47 47	4.82 4.82 4.82 4.82 4.82 4.82 4.82	2 2 2 2 2 2 2	0.52 0.52 0.52 0.52 0.52 0.52 0.52	0.01 0.01 0.01 0.01 0.01 0.01 0.01		[30] [30] [30] [30] [30] [30] [30]
	90201 Chiralcel-CA-1 90201 Chiralcel-CA-1 90201 Chiralcel-CA-1 90201 Chiralcel-CA-1 90201 Chiralcel-CA-1 90201 Chiralcel-CA-1 90201 Chiralcel-CA-1	1	1 1 1 1 1 1	5.31 5.31 5.31 5.31 5.31 5.31 5.31	3 3 3 3 3 3 3	0.82 0.82 0.82 0.82 0.82 0.82 0.82	0.07 0.07 0.07 0.07 0.07 0.07 0.07		[41,42] [41,42] [41,42] [41,42] [41,42] [41,42] [41,42]
		2	2 2 2 2 2 2	4.25 4.25 4.25 4.25 4.25 4.25 4.25	8 8 8 8 8 8 8	0.42 0.42 0.42 0.42 0.42 0.42 0.42	0.29 0.29 0.29 0.29 0.29 0.29 0.29		[43] [43] [43] [43] [43] [43] [43]
	90246 Chiralcel-OJ 90246 Chiralcel-OJ 90246 Chiralcel-OJ 90246 Chiralcel-OJ 90246 Chiralcel-OJ 90246 Chiralcel-OJ 90246 Chiralcel-OJ	1	1 1 1 1 1 1	7.91 7.91 7.91 7.91 7.91 7.91 7.91	5 5 5 5 5 5 5	0.47 0.47 0.47 0.47 0.47 0.47 0.47	0.10 0.10 0.10 0.10 0.10 0.10 0.10		[44-46] [44-46] [44-46] [44-46] [44-46] [44-46] [44-46]
		16	16 16 16 16 16 16	4.59 4.59 4.59 4.59 4.59 4.59 4.59	2 2 2 2 2 2 2	0.45 0.45 0.45 0.45 0.45 0.45 0.45	0.00 0.00 0.00 0.00 0.00 0.00 0.00		[47,48] [47,48] [47,48] [47,48] [47,48] [47,48] [47,48]

Table 2 .

 2 Cont.

	CSP_NO Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297	Cluster		Z		No. Pairs	Mean (Diff)	Std (Diff)	Example	Reference 12 of 27 12 of 27 12 of 27 12 of 27 12 of 27
	Molecules 2016, 21, 1297		19	19	19 19 19 19	4.45 4.45	4.45 4.45 4.45 4.45	2 2	2 2 2 2	0.43 0.43 0.43 0.43 0.43 0.43	0.07 0.07 0.07 0.07 0.07 0.07	[37] [37] [37] [37] [37] [37] 12 of 27
					19		4.45		2	0.43	0.07	[37]
	90292 Pirkle DNB-Leu 90292 Pirkle DNB-Leu 1 90292 Pirkle DNB-Leu 90292 Pirkle DNB-Leu 90292 Pirkle DNB-Leu 90292 Pirkle DNB-Leu	1	1 1 1 1	5.47 5.47	5.47 5.47 5.47 5.47	9 9	9 9 9 9	0.27 0.27 0.27 0.27 0.27 0.27	0.21 0.21 0.21 0.21 0.21 0.21	[49-52] [49-52] [49-52] [49-52] [49-52] [49-52]
	90292 Pirkle DNB-Leu		1		5.47		9	0.27	0.21	[49-52]
			4	4	4 4 4 4	4.41 4.41	4.41 4.41 4.41 4.41	3 3	3 3 3 3	0.38 0.38 0.38 0.38 0.38 0.38	0.01 0.01 0.01 0.01 0.01 0.01	[53] [53] [53] [53] [53] [53]
					4		4.41		3	0.38	0.01	[53]
	90589 Whelk-O 90589 Whelk-O 90589 Whelk-O 90589 Whelk-O 90589 Whelk-O 90589 Whelk-O	10	10	10 10 10 10	4.58 4.58	4.58 4.58 4.58 4.58	2 2	2 2 2 2	0.51 0.51 0.51 0.51 0.51 0.51	0.17 0.17 0.17 0.17 0.17 0.17	[54] [54] [54] [54] [54] [54]
	90589 Whelk-O			10		4.58		2	0.51	0.17	[54]
			12	12	12 12 12 12 12	4.43 4.43	4.43 4.43 4.43 4.43 4.43	4 4	4 4 4 4 4	0.33 0.33 0.33 0.33 0.33 0.33 0.33	0.03 0.03 0.03 0.03 0.03 0.03 0.03	[55] [55] [55] [55] [55] [55] [55]
					13 13 13 13		4.31 4.31 4.31 4.31		2 2 2 2	0.47 0.47 0.47 0.47	0.02 0.02 0.02 0.02	[54] [54] [54] [54]
			13	13	13	4.31 4.31	4.31	2 2	2	0.47 0.47 0.47	0.02 0.02 0.02	[54] [54] [54]

Table 2 .

 2 Cont.

	CSP_NO Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297	Cluster	Z	No. Pairs	Mean (Diff)	Std (Diff)	Example	Reference 13 of 27 13 of 27 13 of 27 13 of 27 13 of 27 13 of 27
		14	14 14 14 14 14 14	4.26 4.26 4.26 4.26 4.26 4.26 4.26	2 2 2 2 2 2 2	0.47 0.47 0.47 0.47 0.47 0.47 0.47	0.05 0.05 0.05 0.05 0.05 0.05 0.05		[56,57] [56,57] [56,57] [56,57] [56,57] [56,57] [56,57]
	90704 Chirobiotic-V 90704 Chirobiotic-V 90704 Chirobiotic-V 90704 Chirobiotic-V 90704 Chirobiotic-V 90704 Chirobiotic-V 90704 Chirobiotic-V	12	12 12 12 12 12 12	4.52 4.52 4.52 4.52 4.52 4.52 4.52	2 2 2 2 2 2 2	0.34 0.34 0.34 0.34 0.34 0.34 0.34	0.34 0.34 0.34 0.34 0.34 0.34 0.34		[58] [58] [58] [58] [58] [58] [58]
		13	13 13 13 13 13 13	4.30 4.30 4.30 4.30 4.30 4.30 4.30	2 2 2 2 2 2 2	0.33 0.33 0.33 0.33 0.33 0.33 0.33	0.36 0.36 0.36 0.36 0.36 0.36 0.36		[58] [58] [58] [58] [58] [58] [58]
		15	15 15 15 15 15 15	4.13 4.13 4.13 4.13 4.13 4.13 4.13	4 4 4 4 4 4 4	0.20 0.20 0.20 0.20 0.20 0.20 0.20	0.25 0.25 0.25 0.25 0.25 0.25 0.25		[58] [58] [58] [58] [58] [58] [58]
		16	16 16 16 16 16 16	4.10 4.10 4.10 4.10 4.10 4.10 4.10	2 2 2 2 2 2 2	0.31 0.31 0.31 0.31 0.31 0.31 0.31	0.26 0.26 0.26 0.26 0.26 0.26 0.26		[58] [58] [58] [58] [58] [58] [58]
	90879 Chirobiotic-T 90879 Chirobiotic-T 90879 Chirobiotic-T 90879 Chirobiotic-T 90879 Chirobiotic-T 90879 Chirobiotic-T 90879 Chirobiotic-T	1	1 1 1 1 1 1	6.57 6.57 6.57 6.57 6.57 6.57 6.57	4 4 4 4 4 4 4	0.70 0.70 0.70 0.70 0.70 0.70 0.70	0.06 0.06 0.06 0.06 0.06 0.06 0.06		[59] [59] [59] [59] [59] [59]
	90879 90879 90879 90879 90879 90879		3 3 3 3 3 3	5.29 5.29 5.29 5.29 5.29 5.29	2 2 2 2 2 2	0.85 0.85 0.85 0.85 0.85 0.85	0.02 0.02 0.02 0.02 0.02 0.02		[59] [59] [59] [59] [59] [59]

Table 2 .

 2 Cont.

			16		4.10			2		0.31	0.26		[58]
	90879 Chirobiotic-T	1		6.57			4		0.70	0.06		[59]
	CSP_NO	Cluster		Z		No. Pairs		Mean (Diff)	Std (Diff)	Example	Reference
	90879	3	3	5.29	5.29		2	2		0.85 0.85	0.02 0.02		[59] [59]
	Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297 Molecules 2016, 21, 1297											14 of 27 14 of 27 14 of 27 14 of 27 14 of 27
	90879	4	4 4 4	4.84 4 4	4.84 4.84 4.84	4.84 4.84	6	6 6 6	6 6	0.41 0.41 0.41 0.41 0.41 0.41	0.30 0.30 0.30 0.30 0.30 0.30		[60] [60] [60] [60] [60] [60]
	90879	5	5 5 5	5 4.76 5	4.76 4.76 4.76	4.76 4.76	2	2 2 2	2 2	0.77 0.77 0.77 0.77 0.77 0.77	0.17 0.17 0.17 0.17 0.17 0.17		[60] [60] [60] [60] [60] [60]
				6 6		4.73 4.73			2 2	0.76 0.76	0.05 0.05		[59] [59]
	90879	6	6 6 6	4.73	4.73 4.73 4.73		2	2 2 2		0.76 0.76 0.76 0.76	0.05 0.05 0.05 0.05		[59] [59] [59] [59]
				10 10		4.62 4.62			4 4	0.49 0.49	0.33 0.33		[58] [58]
	90879	10	10 10 10	4.62	4.62 4.62 4.62		4	4 4 4		0.49 0.49 0.49 0.49	0.33 0.33 0.33 0.33		[58] [58] [58] [58]
				15 15		4.48 4.48			2 2	0.72 0.72	0.01 0.01		[59] [59]
	90879	15	15 15 15	4.48	4.48 4.48 4.48		2	2 2 2		0.72 0.72 0.72 0.72	0.01 0.01 0.01 0.01		[59] [59] [59] [59]

Acknowledgments: This work is dedicated to Stig Allenmark on the occasion of his 80th birthday-friend, collaborator and vital force in the development of the science of chromatographic enantioseparation. We are grateful to the Department of Process & Analytical Chemistry for providing financial support for this collaboration.

Author Contributions: R.P.S., C.J.W. and P.P. conceived and designed the experiments and wrote the paper; E.C.S., C.R., and W.H.P. assisted with the analysis and interpretation of the results.

Supplementary Materials: Supplementary materials can be accessed at: http://www.mdpi.com/1420-3049/21/ 10/1297/s1.

Conflicts of Interest:

The authors declare no conflict of interest.


Maziar Divangahi
email: maziar.divangahi@mcgill.ca

Peter Aaby

Shabaana Abdul Khader

Luis B Barreiro

Siroon Bekkering

Triantafyllos Chavakis

Reinout Van Crevel

Nigel Curtis

Andrew R Dinardo

Jorge Dominguez-Andres

Raphael Duivenwoorden

Stephanie Fanucchi

Zahi Fayad

Elaine Fuchs

Melanie Hamon

Kate L Jeffrey

Nargis Khan

Leo A B Joosten

Eva Kaufmann

Eicke Latz

Giuseppe Matarese

Jos W M Van Der Meer

Musa Mhlanga

Simone J C F M Moorlag

Willem J M Mulder

Shruti Naik

Boris Novakovic

Luke O'neill

Jordi Ochando

Keiko Ozato

Niels P Riksen

Robert Sauerwein

Edward R Sherwood

Andreas Schlitzer

Joachim L Schultze

Michael H Sieweke

Christine Stabell Benn

Henk Stunnenberg

Joseph Sun

Frank L Van De Veerdonk

Sebastian Weis

David L Williams

Ramnik Xavier

Mihai G Netea
email: mihai.netea@radboudumc.nl.

Trained

Keywords: immunity, tolerance, priming and differentiation: distinct immunological processes

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The similarities and differences between trained immunity and other immune processes are the subject of intense interrogation. Therefore, a consensus on the definition of trained immunity in both in vitro and in vivo settings, as well as in experimental models and human subjects, is necessary for advancing this field of research. Here we aim to establish a common framework that describes the experimental standards for defining trained immunity.

Trained immunity has been defined as one form of adaptation of innate host defense mechanisms or a de facto innate immune memory. Following exposure to particular infectious agents or vaccines, trained immunity can mount a faster and greater response against a secondary challenge with homologous or even heterologous pathogens 1 . Trained immunity has emerged as a focal point in immunology research and has added a layer of complexity to our previous understanding of immune memory, that is, a trait limited to antigen-specific responses of the adaptive immune system. Although more than 95% of species (plants and invertebrates) rely solely on innate immunity for host defense 2 , immunological memory has been associated mainly with the adaptive arm of the immune response in vertebrates. However, it is highly unlikely that a critical evolutionary trait like immunological memory is restricted to adaptive immunity and has not evolved in the innate arm of immunity in the entire spectrum of living organisms. In fact, systemic acquired resistance (SAR) is a well-defined state corresponding to innate immunological memory in plants 3 . Similarly, the innate immune system of invertebrates (for example, mosquitoes, the bumble bee Bombus terrestris, snails, and so on) has the capacity to generate memory responses to subsequent reinfection with the same or different pathogens 1 . There is also compelling evidence in animal models that an initial infection or vaccination with bacteria (for example, Bacille Calmette-Guérin (BCG)), fungi (for example, Candida albicans) or helminth parasites (for example, Nippostrongylus brasiliensis) protects against heterologous infections independently of adaptive immunity 1 . Furthermore, while the rationale underlying the use of adjuvants in vaccine formulations is to improve the efficacy of adaptive immunity, little attention has been given to the direct effects of adjuvants on innate immunity and early protection against infection. For instance, β-glucan (mainly encountered as a component of fungal cell walls that activates dectin-1) enhances resistance to acute infection with Staphylococcus aureus 4 or chronic infection with Mycobacterium tuberculosis (Mtb) 5 . Similarly, administration of agonists of NOD-like receptors (for example, NOD2) or Toll-like receptors (for example, TLR9) have been shown to provide protection against Toxoplasma 6 and sepsis caused by Escherichia coli 7 , respectively. Intriguingly, the induction of trained immunity is regulated by a unique set of mediators. For instance, BCG-mediated trained immunity requires type II interferon (IFN) 8 , Mtb impaired trained immunity via type I IFN 9 , and the inflammatory cytokines interleukin (IL)-1 and GM-CSF were essential for β-glucan-induced trained immunity 10 . In addition to what has been seen in experimental animal models, there is now ample evidence that trained immunity is a component of the human host response to pathogens. Epidemiological studies have shown that vaccination with certain live vaccines provides heterologous protection against unrelated pathogens. For example, BCG vaccination in newborn children provides protection not only against tuberculosis but also against respiratory tract infections and neonatal sepsis, and it significantly reduces mortality 1 . BCG-induced trained immunity has also been shown to provide protection against experimental models of yellow fever 11 and malaria 12 infection. Interestingly, the anticancer effects of BCG (for example, in bladder cancer) have also been linked to trained immunity 13 . Thus, trained immunity is an evolutionary trait that increases the fitness of plants, invertebrates and vertebrates against pathogenic microbes.

It is also important to emphasize that, although trained immunity improves the host's defense against subsequent pathogenic threats, it may also be maladaptive in the context of chronic inflammatory disease, such as atherosclerosis 1 . Indeed, in addition to microbial products, trained immunity can also be induced by endogenous atherogenic substances, including oxidized low-density lipoprotein particles, lipoprotein (a) and catecholamines 1,14 . In animal models of atherosclerosis, a Western-type diet induces trained immunity, which persists even after a switch to a healthy chow diet 15 . Nevertheless, it is remarkable that trained immunity induced by BCG vaccination has also been involved in improved induction of immune regulation and immunological self-tolerance in models of autoimmunity such as type 1 diabetes and multiple sclerosis 16 . While the precise molecular mechanisms are still not fully understood, this evidence supports the broad rationale that exposure to microbes may help to antagonize conditions sharing chronic inflammation and tissue damage, such as autoimmunity. The beneficial effect of BCG vaccination, at least in some autoimmune disorders, is an interesting facet of host-microbe interplay in the pathophysiology and treatment of immune-mediated diseases.

Mechanisms of trained immunity

Immunological memory in the two arms of host defense is mediated through different processes. During the induction of adaptive immune memory, two properties are induced at the same time: (1) the specificity of the response, ensured through the rearrangement of immunoglobulin family genes and clonal expansion; and (2) the amplitude and speed of the response, mediated by epigenetic reprogramming that modulates the kinetics of gene transcription 1 . By contrast, the mechanisms involved in innate memory responses depend solely on epigenetic remodeling, and trained immunity appears to be devoid of specificity. It has been proposed that immune memory in innate and adaptive immunity represents an evolutionary continuum in which a more robust immune response evolved first, mediated by epigenetic mechanisms, while specificity evolved later in a subgroup of species (vertebrates) through gene recombination 2 .

While some of the mechanisms for epigenetic remodeling and metabolic reprogramming during trained immunity have been recently reviewed 1 , the duration and maintenance of chromatin-driven innate memory responses are still the subject of intense investigation. In the context of infectious disease, there are three known factors that can influence the epigenetic programming of an immune cell: (1) direct infection, (2) pathogen-associated molecular patterns (PAMPs) from microorganisms, and (3) cytokines released during the induction of the host response. We envision that these key factors impact the duration of trained immunity at a central level, in hematopoietic stem and progenitor cells (HSPCs) in the bone marrow (BM) and in circulating immune cells, and peripherally, at the tissuespecific level. It has been demonstrated that BCG, β-glucan, and a Western-type diet reprogram BM-HSPCs toward myelopoiesis and generate trained immunity 8,10,15 . These studies provide an explanation for why short-lived innate immune cells can acquire memory with a persistent phenotype in vivo. However, this may also impact the replacement of tissue-resident innate immune cells with new and reprogrammed HSPCs. It has been shown that, following pulmonary insults, a reduction in yolk-sac-derived alveolar macrophages is compensated for through the accumulation of BM-derived macrophages in the lung airways 17 . For example, infection of mice with gammaherpesviruses provided protection against allergic asthma, as it caused resident alveolar macrophages (AMs) to be replaced with BM-derived AMs 18 . Thus, a new imprinted AM may have a completely different functional capacity to that of an original fetal-derived AM. It has also been shown that the metabolism of AMs is significantly different from that of bone-marrow-derived macrophages (BMDMs), the latter being more glycolytic and bactericidal 19 . In addition, a murine model of pulmonary adenoviral infection induces trained immunity in AMs, which is dependent on T cells but independent of BMDMs 20 . Furthermore, the inflammatory site may also alter the functional capacity of the local stromal cells that induce trained immunity in residential innate immune cells. Strikingly, following skin inflammation, epithelial stem cells maintain prolonged chromatin accessibility at key inflammatory genes. This feature expedites and heightens their response to subsequent stressors and potentially influences stem cell cross-talk with trained immune cells 21 . Therefore, central and peripheral factors, or a combination of both, can impact the duration and maintenance of trained immunity.

Differentiation, priming, tolerance and training

Adaptations in innate immune compartments are exceptionally diverse, as innate immune cells demonstrate substantial plasticity and adapt to various insults such as trauma, infections and vaccination, and they continue to adapt as they leave the local microenvironment of the bone marrow and travel to the blood and tissues. It is important to note that the magnitude (low versus high dose) and duration (short versus long) of stimulation induces specific adaptations in innate immune cells that reflect their requirement to either enhance immune responses or prevent immunity and excessive immunopathology. Several such adaptive programs have been described, including cell differentiation, priming, tolerance and trained immunity (Fig. 1). Because innate immune cells can undergo any of these functional adaptive programs, it is essential to precisely define the similarities and differences between these cellular adaptations to ensure the field's focus and avoid confusion in the literature.

The main difference between innate immune cells undergoing these different adaptive programs is their functional status prior to secondary challenges. Innate immune cell 'differentiation' (Fig. 1a) is often the change of an immature cell into its mature counterpart, which is defined by a long-term change in the functional program of the cell and is often accompanied by altered morphological characteristics caused by alterations of the tissue environment or chronic exposure to stimuli 22 . During 'priming' (Fig. 1b), the first stimulus changes the functional state of these cells, and their immune status (as defined by active gene transcription) does not return to basal levels before the secondary stimulation or infection. Thus, the impact of a second challenge in primed cells is often additive or synergistic with the original stimuli. In 'trained immunity' (Fig. 1c), in sharp contrast to priming, while the first stimulus leads to changes in the functional immune status, the immune activation status returns to the basal level following removal of the stimulus, while the epigenetic alterations persist. However, in response to homologous or heterologous challenges, both gene transcription and cell function are enhanced at much higher levels than those observed during the primary challenge. The opposite of trained immunity is innate immune 'tolerance,' wherein the cell is unable to activate gene transcription and does not perform its functions following restimulation (Fig. 1d). For instance, repeated or persistent exposure of macrophages to a high dose of lipopolysaccharide epigenetically enforces tolerance to prevent the expression of inflammatory genes 23 .

Therefore, studies aiming to investigate trained immunity need to clearly identify the activation state during initial stimulation (represented by effector functions such as cytokine and reactive oxygen species production, phagocytosis, killing, and so on) as well as after the removal of the initial insult. This is challenging when dealing with monocytes and macrophages, as their spectrum of states is far less defined than that of adaptive immune cells. However, improved nomenclature has been proposed for both in vitro and tissueresident monocytes and macrophages 24 . To investigate the central effects of trained immunity via HSPCs in the BM, experiments have been conducted predominantly in vivo or ex vivo 8,10 . However, many in vitro experiments have been performed to study the peripheral impacts of trained immunity. Thus, experimental standards are necessary for expanding our knowledge in this exciting field of immunology.

Defining the adaptive programs of innate immune cells according to their functional state is important because the molecular mechanisms underlying these processes can often overlap. In this respect, the epigenetic and metabolic rewiring that specifically program cell differentiation, trained immunity, immune priming and tolerance can define these processes. Thus, there are unique signatures that define different cellular adaptations. For example, long-term changes in DNA methylation and stable changes in chromatin accessibility can accompany cell differentiation, whereas specific histone marks characterizing 'latent enhancers,' such as monomethylated histone H3 K4 (but not solely that), are often 'tagged' in trained immunity 1 . However, while specific pathways and markers differ between the various adaptive programs in innate immunity, they all use the same basic mechanisms (epigenetic, transcriptional and metabolic), but with different flavors.

Experimental models of trained immunity

The models used to study the adaptive programs in innate immunity, including trained immunity, should, therefore, reflect the definitions of these processes (Fig. 2). In vitro and in vivo models have been predominantly used to study peripheral trained immunity. The most common in vitro model system of trained immunity is the training of human peripheral monocytes, in which these cells are exposed to a stimulus (training period) for a short period of time (usually 24 h). Subsequently, cells are incubated for 5-7 days in culture medium without any stimulation. During this resting period, the functional program of the cells returns to steady state. If the primary training stimulus results in epigenetic encoding of these differentiated macrophages, they will show a heightened response to homologous or heterologous secondary stimuli. These models are fundamentally different from models of cell differentiation or priming in which the stimulus is either maintained for a long period of time to induce differentiation/priming or secondary stimulation is performed very quickly after the initial priming. In these cases, in contrast to training, cells are not allowed to return to the functional steady state before secondary stimulation.

Similar considerations are valid for models of the induction of trained immunity in vivo, although different transcriptional and epigenetic changes can be seen in mature innate immune cells in the periphery or in bone marrow progenitors of innate immune cells. To reprogram BM-HSPCs for central trained immunity, administration of training agents (for example, β-glucan) or a vaccine (for example, BCG) leads initially to proliferation and expansion of HSPCs. Although the kinetics and dynamics of this expansion differ between model systems, HSPCs phenotypically return to a basal status before a secondary challenge experiment (Fig. 2). Murine models of trained immunity allow us to study the direct impact of BM-HSC training on the immune response to a new homologous or heterologous challenge. One current possibility to study this in vivo is to generate a chimeric or serial engraftment mouse model wherein the HSC compartment is reconstituted with trained HSCs [8][9][10] . As the reconstitution of hematopoiesis in the recipient is mediated by donor hematopoietic progenitors at earlier time points and entirely by the donor HSC compartment at later time points (16 weeks) post-transplantation, this system is an excellent model to study the long-term effects of training on HSC-mediated innate memory responses to subsequent homologous or heterologous challenges 9 .

Human models of trained immunity have also been established as proof of concept to study innate immune memory. BCG vaccination in healthy individuals can induce trained immunity in HSCs and circulating monocytes, which gain an enhanced protective capacity against a range of infectious agents 1 . The function of innate immune cells, as well as their epigenetic and transcriptional programs, can be studied before and after BCG vaccination (or after any other vaccine or experimental infection, for that matter). However, to ensure that the initial vaccine has been cleared from the organism, it is important that there is a sufficient interval between vaccination and the subsequent assessment of the innate immune system by ex vivo restimulation with a non-specific stimulus. As an example, BCG may be present at the site of vaccination for up to one month 25 , thus trained immunity assessments should be performed at later time points. During the first month after vaccination, the ex vivo-stimulation assay can actually be a model for studying innate immune priming. Furthermore, an ex vivo system could also be used to study (1) the bias of hematopoiesis (for example, myelopoiesis versus lymphopoiesis) in HSPCs using colony-forming unit assays and (2) the functional capacity of myeloid immune cells (for example, macrophages or neutrophils).

Perspectives and conclusions

Understanding innate immune memory is critical for deciphering new approaches to vaccine development. By dissecting the cellular and molecular mechanisms of trained immunity, we hope to develop new vaccine strategies with cross-protective efficacy against a range of infections. In addition, we can envisage more effective vaccines that combine the induction of trained immunity with adaptive immune memory. While we have made enormous progress in our fundamental understanding of trained immunity in health and diseases, accurate nomenclature and experimental standardization are important and would encourage progress in the field. By doing this work, we hope scientists who are new to trained immunity will establish accurate experimental models to close the knowledge gap in this field. Furthermore, as a more precise mechanistic description of trained immunity is developed, we will need to formulate updated recommendations on trained immunity. Experimental models of in vitro and in vivo trained immunity.

Fig. 1 |

 1 Fig. 1 |. Schematic presentation of the behavior of innate immune responses during the different adaptive programs induced in innate immune cells. a, Differentiation. b, Priming. c, Trained immunity (innate immune memory). d, Tolerance.

Fig. 2

 2 Fig. 2 |.

Nat Immunol. Author manuscript; available in PMC 2022 January 01.

Competing interests

The authors declare no competing interests.