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Sorting measurement queues to speed up the flow injection analysis mass spectrometry of combinatorial chemistry syntheses
Analytica Chimica Acta 394 (1999) 33±42
Sorting measurement queues to speed up the ¯ow injection analysis mass spectrometry of combinatorial chemistry syntheses Ramsay Richmond*, Ekkehard GoÈrlach Core Technology Area Analytics unit, Novartis Pharma AG, CH-4002 Basel, Switzerland Received 4 January 1999; received in revised form 12 March 1999; accepted 13 March 1999
Abstract A high-throughput ¯ow injection analysis mass spectrometry system was developed for the purity estimation of multiple parallel combinatorial chemistry synthetic samples. Surreptitious inter-sample carry-over represents a threat to the accuracy of the purity estimates. The visualisation of carry-over in 96-well racks via a surveillance program was improved by introducing the automatic sorting of the intra-rack sample measurement queues. This was necessary as the logical arraying of building blocks by synthetic dispensing robots imparts an underlying row and/or column order to the 96-well racks, which complicates the estimation of carry-over. In a primary sorting step, the molecular weight difference between the expected synthetic products in consecutively measured wells was maximised; in a secondary sorting step, the consecutive measurement of wells with similar building blocks was minimised. Over four hundred samples drawn equally from ®ve diverse combinatorial synthetic families were measured to explore the use of this two-stage sorting to ease the measurement of carry-over, and so to facilitate the optimisation of the ¯ow injection analysis measurement duty cycle. Subsequently the duty cycle in daily use, of 168 s was reduced to 70 s, while maintaining the median inter-sample carry-over at levels below 1%. # 1999 Elsevier Science B.V. All rights reserved. Keywords: Combinatorial chemistry; Flow injection analysis; Mass spectrometry; Optimisation; Measurement sequence; Duty cycle
1. Introduction In combinatorial chemistry two strategies are available to synthesise a library of diverse compounds for bioassay screening: split and mix synthesis, producing mixtures and multiple parallel synthesis, generally producing single compounds. Three years ago, the purity estimation of large numbers of the latter started to confront the Novartis Pharma MS group. We adopted ¯ow injection analysis (FIA) combined with *Corresponding author. Tel.: +41-61-324-6756; fax: +41-61324-5930; e-mail: [email protected]
mass spectrometry (MS) as a starting point [1,2]. Commercial FIA-MS systems with bundled automatic purity estimation software were not then available and so this obliged the in-house development of a highthroughput FIA-MS system along with a networked results reporting system. Electrospray (ESI) was chosen as the ionisation mode for purity estimation of spectra against expected molecular monoisotopic weights. ESI adduction is preferable to the risk of unanticipated fragmentation induced by atmospheric pressure chemical ionisation (APcI), as the large sample numbers effectively prevent comprehensive manual inspection of the spectra.
0003-2670/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved. PII: S 0 0 0 3 - 2 6 7 0 ( 9 9 ) 0 0 2 5 6 - 1
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R. Richmond, E. GoÈrlach / Analytica Chimica Acta 394 (1999) 33±42
Our goal was to deliver these purity estimates in a quick and clear way to the spread out combinatorial chemistry customers, via their networked PCs. Mass spectrometry analysis of the customer's 96well rack samples generates an excessive wealth of chemical information. To reduce this to manageable proportions, colour rendering of the data within the Visual Basic RackViewer interface program is used, with numerical purity estimates relegated to the (accessible) background [1,2]. An intrinsic problem of estimating the purities of very large numbers of samples in high-throughput FIA-MS systems is that the operator's familiarity with individual measurements no longer exists. Inter-sample carry-over can therefore appear and escape detection if automatic surveillance systems are not implemented, where inter-sample carry-over is speci®cally de®ned as ion current in a spectrum that can be attributed to the preceding sample in the measurement sequence [3]. There are many reports in the literature describing FIA linked to thermospray, ESI and APcI [4±17]. Optimisation of the MS [14,15] and FIA parts [16] has been reported. Interestingly, inter-sample carry-over does not appear to have been discussed or numerically reported in the FIA-MS literature. Faced with estimating the purities of large numbers of combinatorial chemistry samples, lacking both a priori view of their analytical handling characteristics and operating in a scienti®c ®eld with no previous published literature, forced us to develop our own inter-sample carry-over measurement tools. This involved a novel colourised visualisation of carry-over within RackViewer, for the automated quality control surveillance of large numbers of 96well racks [3]. In this report, the visualisation of carry-over in 96well racks was improved by introducing the automatic sorting of the intra-rack sample measurement queues. This was necessary as the logical arraying of building blocks by the combinatorial chemistry dispensing robots imparts an underlying row and/or column order to the 96-well racks, which complicates the estimation of carry-over e.g. the overlapping of quasi-molecular isotope clusters in consecutive measurements. A primary sorting was developed to maximise the molecular weight difference between the expected synthetic products in consecutively measured wells and a secondary sorting to minimise the consecutive
measurement of wells with similar building blocks. In a pilot study, ®ve different combinatorial chemistries from ®ve different laboratories (racks 165, 275, 508, 700 and 998), chosen for their comprehensive coverage of the analytical handling characteristics of the corporate multiple parallel synthesis programme, were examined. Four sets of FIA-MS conditions were applied, i.e. ESI with a slow duty cycle of 168 s and non-sorted queues, secondly ESI with a slow duty cycle of 168 s and sorted queues, thirdly ESI with a fast duty cycle of 100 s and sorted queues and lastly ESI with very fast duty cycle of 70 s and sorted queues. These are designated as FIA-MS systems A1, A3, A4 and A5, respectively. The ESI with the slow duty cycle and non-sorted queues, i.e. A1 has been our default method in daily use for the last two years [1±3]. 2. Experimental 2.1. Mass spectrometry A Finnigan MAT SSQ-7000 single quadrupole mass spectrometer (San Jose, USA) was used in these experiments, controlled by a DEC AXP 3000/300 workstation under the OSF/1 V3.2D operating system running the Finnigan Interactive Chemical Information System (ICIS) V8.2.1 application software. Two Ethernet-interfaces connected the workstation to a SSQ-7000 and the Novartis local area network (LAN), respectively. A dedicated ®le service accessible by the synthetic chemists was set up on the Novartis LAN, to exchange ®les with that area via TCP/IP (Samba). The Finnigan ICIS package controls a Hewlett Packard 1090 series liquid chromatograph through a HP-IB interface and the autosampler via the RS-232C interface. An autosampler with capacity for eight 96well racks was used (model HTS PAL from CTC Analytics, CH-4222 Zwingen, Switzerland). The maximum overhead gantry speed of the autosampler is 30 cm/s. The CTC installation software, v1.3.135 enabled the six ports Valco valve inject/load orientation to be veri®ed prior to every injection. The MS operating conditions in ESI were: positive ion mode; heated capillary temperature, 2208C; conversion dynode at 15 kV; electron multiplier at 0.9 kV,
R. Richmond, E. GoÈrlach / Analytica Chimica Acta 394 (1999) 33±42
a collision induced dissociation offset of 0.0 V and a spray voltage 4.5 kV. The FIA solvent was CH3CN:H2O 70:30 (v/v), with gradient grade acetonitrile and `water for chromatography' from Merck (D-64271, Darmstadt, Germany). The FIA solvent ¯ow rate was 0.05 ml/min. A 55 cm long PEEK tubing of dimensions 0.127 mm i.d.1.588 mm o.d. (Upchurch Scienti®c, part no. 1535) linked via a zero-dead volume union with a 40 cm long ESI fused silica capillary of dimensions 200 mm o.d.100 mm i.d. (Polymicro Technologies, Phoenix, Arizona 85023-1200, part no. TSP100200), joined the autosampler Valco valve to the exit end of the electrospray spray nozzle (Finnigan part no. 70005-20169) [1,2]. The head pressure for the ESI source nitrogen sheath gas was 413 kPa. The Finnigan API interface version [18,19] is characterised by the dimensions of the source heated capillary, i.e. 0.406 mm i.d.1.588 mm o.d. (Finnigan part no. 70005-98038). A 0.001 mg/ml solution of yohimbine hydrochloride in CH3CN:H2O 70:30 (v/v), infused at 0.05 ml/min was used to tune in ESI. A scan range of 160±1560 m/z per 2 s was used, with centroid data acquisition. This range is wide enough to include any gas dimers for purity estimation. This range avoids three occasional but highly ESI responding contaminants, i.e. [DMSO gas dimerH], [triethylamineH], [triethylamine HCH3CN] and [N-ethyldiisopropylamineH]. 2.2. Rack delivery and preparation The 96-well sample racks arrive for MS analysis, logically arrayed [3,17] by row and column according to their building blocks. The samples are usually of solid-phase origin, and in a relatively high concentration. Parallel to the physical delivery of the racks, Microsoft Excel ®les describing each rack are deposited on a central Microsoft NT server by the combinatorial chemists. The Excel ®les are then called over the network by the MS operator and UNIX script ®les then automatically build the mass spectrometer's analysis page methods; also included is the automatic set up of the MS parameters. This includes the individual synthetic chemist's name, rack's name, desired ionisation mode, location of occupied wells with their formulae and calculation of expected monoisotopic quasi-molecular and adduct m/z positions. The 96-
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well racks are usually Micronic1Blueline racks from Micronic BV, PO Box 604, 8211 Ap Lelystad, The Netherlands. 2.3. Data handling The two-stage sorting program [20] for the measurement sequence was written in Visual Basic for Microsoft Excel (v5.0c), to process the sample submitting chemist's Excel CSV format ®le that accompanies each 96-well rack. The median values of the carry-over populations for the FIA-MS systems were calculated using SYSTAT v7.0.1 for Windows from SPSS, 444 North Michigan Avenue, Chicago, IL 60611. The box plots [21,22] were plotted using SYSTAT; the inner and outer fence de®nitions for the box plot speci®cally use the recommendations in [21]. 3. Results 3.1. Primary sort Ion current included in the carry-over calculations can originate from at least seven ion current categories; (I) real carry-over from the previous sample; (IIa) artefactual carry-over due to completely overlapping, i.e. isomeric quasi-molecular isotope clusters in consecutive wells; (IIb) artefactual carry-over due to partially overlapping quasi-molecular isotope clusters in consecutive wells; (IIc) artefactual carry-over due to overlapping adduct isotope clusters in consecutive wells; (IId) artefactual carry-over due to fragmentation to identical m/z ions in consecutive wells; (IIe) artefactual carry-over due to quasi-molecular or adduct ions overlapping with fragment ions in consecutive wells (or vice-versa); (III) artefactual carryover due to trace levels of high ionisation responders creeping into the end-product solution, e.g. triethylamine. In the upper screen-shot in Fig. 1, columns 3, 6, and 12 show artefactual carry-over due to inclusion of ion current categories IIb, IIa and IIe, respectively. In the upper screen-shot in Fig. 2, the vertical battlement pattern is due to ion category IIa. All of these are excluded after the two-stage sorting of the measurement sequence, i.e. the lower screen-shots in Figs. 1 and 2.
36 R. Richmond, E. GoÈrlach / Analytica Chimica Acta 394 (1999) 33±42 Fig. 1. The upper screenshot is a RackViewer/Overview of automatic fitted % carry-over for rack 508, using ESI with the slow duty cycle (A1) and non-sorted queues, as in Fig. 3. The colourisation criteria are red for 20%, amber for 5 to <20%, light green for 2 to <5% and dark green <2% carry-over. The lower screenshot is a RackViewer/Overview of automatic fitted % carry-over for rack 508 using ESI with the very fast duty cycle and sorted queues (A5), as in Fig. 5. This has the highest carry-over in column 5, Table 2. The first well measured in each rack has conventionally no carry-over estimate and is coloured white.
R. Richmond, E. GoÈrlach / Analytica Chimica Acta 394 (1999) 33±42 Fig. 2. The upper screenshot is a RackViewer/Overview of automatic fitted % carry-over for rack 700, using ESI with the slow duty cycle (A1) and non-sorted queues, as in Fig. 3. The lower screenshot is a RackViewer/Overview of automatic fitted % carry-over for rack 700 using ESI with the very fast duty cycle and sorted queues (A5), as in Fig. 5. This has the lowest carry-over in column 5, Table 2. The first well measured in each rack has conventionally no carry-over estimate and is coloured white. 37
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The default intra-rack measurement queue over the last two years, uses a right to left and top to bottom sequence, i.e. wells B1±B12, C1±C12,. . .H1±H12. The majority of 96-well racks facing us have logical arraying of the synthetic building blocks, giving an underlying row and/or column order. This is clearly unsuitable for optimal estimation of inter-sample carry-over, as there is a risk of categories IIa, IIb, IId, IIe being included in the carry-over calculations. From experimental experience, the risk is also run of including ion current from category III. A recent report [16] describes ultra-high throughput FIA-MS and the simultaneous loading of entire eight well columns from 96-well racks onto a multi-loop injector. This seems ill-conditioned for measuring carry-over, assuming an underlying building block order, i.e. logical arraying by row and/or column. Therefore the default measurement sequence B1± H12 was sorted in ascending order on the basis of molecular weight. This is considered the primary sorting and was done within Microsoft Excel, using the customer submitted Excel CSV format input ®le. The ®rst half of the sorted list was then rif¯e shuf¯ed [20], with the second half of the sorted list, i.e. ®rst well of ®rst half paired with ®rst well of second half, second well of ®rst half paired with second well of second half and so on. This maximises the molecular weight difference between consecutively measured wells for the entire rack. Therefore the quasi-molecular ion regions in consecutive wells will be as far apart as possible and this should minimise the risk of including ion current categories IIa, IIb and IIc.
However consecutive wells can occasionally have the same row or column after this sort, e.g. wells B12, F12 or G5, G12, see third column, Table 1. This runs the risk of ion current categories IId and III being included in carry-over calculations. Therefore a secondary sorting was developed. If a row or column similarity is found in any consecutive pairs in the measurement sequence, the culprit well, deemed as the second in the consecutive pair, was exchanged with the well two ahead in the measurement sequence. If however this exchange simply recreates this row or column similarity event, then the exchange is reset, and successive attempts are made with wells four, six and eight ahead. If this fails at the fourth try, then the sorting proceeds to the next row or column similarity event. Once the forward similarity sorting is ®nished, an identical sorting in the reverse direction is made, see Table 1. Columns 8 and 9 in Table 1 show the necessity of the reverse sorting to eliminate all occurrences of row or column similarity in consecutively measured wells. For ease of comparison, box plots rather than colourised RackViewer/Overviews are used to depict the carry-over distributions for the four FIA-MS systems A1, A3, A4 and A5. The carry-over data was processed using a 4 a.m.u. ®lter for consecutive well molecular weights to eliminate artefactual ion current. The carry-over distributions in the four FIA-MS systems are highly skewed and contain outliers. Box plots are insensitive to outliers and display the following features of a distribution; median, spread, skewness, tail length and outliers. Also, since the median is
Table 1 Indices for five different racks at the initial, two intermediate steps and end of two-stage sortinga Rack
165 275 508 700 998 a
Primary sorting cumulative molecular weight differences for the entire rack i.e. (|mw|)
Secondary sorting row or column similarity events
Initial
After mw sort
After forward sort
After reverse sort
Initial
After mw sort
After forward sort
After reverse sort
2900 5163 4222 1466 2066
5319 12030 7880 3969 4406
5326 12040 7880 3969 4406
5326 12040 7880 3969 4406
77 77 77 77 77
4 19 14 17 15
1 0 0 4 2
0 0 0 0 0
The goal of the sorting is the minimisation of row and column similarity and the maximisation of molecular weight difference in consecutive measurements. The sorting sequence in time is from left to right. The final indices are in the fifth and the ninth columns. The majority of changes occur in the first molecular weight sorting.
R. Richmond, E. GoÈrlach / Analytica Chimica Acta 394 (1999) 33±42
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Table 2 Median inter-sample % carry-overs for ESI with slow duty cycle and non-sorted queues (A1); ESI with slow duty cycle and sorted queues (A3); ESI with fast duty cycle and sorted queues (A4); ESI with very fast duty cycle and sorted queues (A5) Rack
FIA-MS system A1 median % carry-over
FIA-MS system A3 median % carry-over
FIA-MS system A4 median % carry-over
FIA-MS system A5 median % carry-over
165 275 508 700 998 Pooled
0.82, 0.16, 0.91, 1.10, 1.07, 0.74,
0.36, 0.07, 0.07, 0.02, 0.00, 0.06,
0.30, 0.05, 0.11, 0.04, 0.00, 0.06,
0.18, 0.10, 0.36, 0.00, 0.00, 0.01,
n69 n83 n69 n48 n48 n317
n83 n83 n83 n82 n83 n414
n83 n83 n83 n82 n83 n414
n83 n83 n83 n82 n83 n414
A 4 a.m.u. filter for consecutive well molecular weights was used to eliminate ion categories IIb and IIc in all four systems. The FIA-MS system A1 is the `ESI with intervening washes' described in [3] and used as the default system in [1,2].
relatively insensitive to outliers compared to the mean, the former was used here as the de®ning parameter for carry-over. In Table 2, there is a clear reduction in the median inter-sample carry-over of all ®ve racks, going from using non-sorted measurement queues of FIA-MS system A1 (0.74%) to the sorted queues of system A3 (0.06%). But there is no clear difference in the median carry-over, going from the system A3 slow duty cycle of Fig. 3, to the system A4 fast duty cycle (0.06%) of Fig. 4, and then to the system A5 very fast duty cycle (0.01%) of Fig. 5. Therefore the last was adopted as our new default FIA-MS system for daily
use. The relative standard deviation of the carry-over estimates derived from measurements (n5) of the entire column 6 in rack 508 (Fig. 1) by FIA-MS system A1, is 9.46%. The relative standard deviation of the purity estimates is 7.42%, derived from measurements (n10) of the entire column 6 in rack 508 [1±3]. 3.2. Optimisation of the FIA-MS measurement duty cycle In the slow FIA-MS duty cycle used in [1,2] a major consideration was that the wash injection in the FIA
Fig. 3. Slow FIA-MS duty cycle with injection variation as in A1 and A3. Sample and wash injections are controlled from the MS and loop washes from the autosampler. Flow rate50 mls/min and peak elutes at 20 s. The duty cycle of 168 s is punctuated as follows: (1a) sample acquisition start; (2a) sample acquisition stop; (3a) wash acquisition start; (4a) wash acquisition stop; (5a) next sample acquisition start.
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R. Richmond, E. GoÈrlach / Analytica Chimica Acta 394 (1999) 33±42
Fig. 4. Fast FIA-MS duty cycle with injection variation as in A4. Sample and wash injections are controlled from the MS and loop washes from the autosampler. Flow rate50 mls/min and peak elutes at 20 s. The duty cycle of 100 s is punctuated as follows: (1a) sample acquisition start; (2a) sample acquisition stop; (3a) next sample acquisition start.
stream should be acquired as a data ®le. The logic was that it provided an alternative approach for measuring carry-over risk compared to the direct ®tted method in [3]. The total ion current of the spectrum from this
wash FIA pulse could be measured and compared with that of the next sample. The resultant ratio should provide an estimate of the maximum carry-over risk. An experimental survey of the ®ve different combi-
Fig. 5. Very fast FIA-MS duty cycle with respective injection variation as in A5. Sample and wash injections are controlled from the MS and loop washes from the autosampler. Flow rate50 mls/min and peak elutes at 20 s. The duty cycle of 70 s is punctuated as follows: (1a) sample acquisition start; (2a) sample acquisition stop; (3a) next sample acquisition start.
R. Richmond, E. GoÈrlach / Analytica Chimica Acta 394 (1999) 33±42
natorial chemistries, using a RackViewer visualisation window depicting this TICwash/TICsample ratio, showed this approach's clear failings. Speci®cally this `extrapolative maximum carry-over risk' strategy grossly exaggerated the real carry-over, probably due to non-linearity in the ESI dynamic response curves for the analytes involved. Once this was realised, the necessity of data acquisition of the intervening washes fell away. However the experiments in [3] demonstrated the apparent necessity of at least some of the physical elements of the wash injection sequence in Fig. 3, to maintain the median intersample carry-over at <1%. Therefore the wash injection analyte pulse resulting from the valve wash, was positioned into a non-acquisition zone immediately after the sample acquisition (delay no. 1, Fig. 4). This allowed the duty cycle to be initially reduced from 168 to 100 s. Experimentation to further reduce the measurement duty cycle, involved the elimination of this second injector valve switching into the solvent stream, i.e. FIA-MS system A5, while retaining the second loop wash (Fig. 5). This allowed a further reduction of the duty cycle to 70 s, while maintaining inter-sample carry-over at levels below 1%, see median cross bars in Fig. 6.
Fig. 6. Box plots of automatic fitted carry-over for FIA-MS systems A1, A3, A4 and A5, pooled for all five 96-well racks. The cross-bar with the box is the median; upper and lower box edges enclose 50% of distribution. The median cross-bar in A5 has visually merged with the lower box edge. Asterixes and circles represent outliers and far outliers, respectively [21,22]. Only consecutive wells with a | molecular weight| >4 a.m.u. were used, to exclude grossly artefactual ion current.
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The autosampler used in this study has an overhead gantry containing the sampling syringe. This architecture has certain implicit time imperatives, especially evident in the very fast duty cycle (Fig. 5). The syringe's resting position to 96-well rack and then to injector involves a gantry movement taking 28 s. The delay for a subsequent loop wash involves another movement needing 17 s. Therefore in the very fast duty cycle, (Fig. 5), the autosampler's mechanical movement involves a minimum of 62 s, occupying 89% of the duty cycle. The residual 11% is used for positional calibration of the autosampler's injector head. 4. Conclusions Logical arraying of building blocks by combinatorial chemistry dispensing robots complicates the accurate estimation of inter-sample carry-over in combinatorial chemistry racks. This in turn hinders the optimisation of the FIA-MS measurement duty cycle. A novel visualisation program for monitoring surreptitious inter-sample carry-over combined with a two-stage sorting of intra-rack measurement queues, facilitated an incremental decrease in the FIA-MS measurement duty cycle, from 168 to 100 s and then to 70 s. The median inter-sample carry-over was maintained at less than 1%. FIA-MS systems are comprised of many physical components interacting in a speci®ed time sequence, e.g. loop type and size, number of loop washes, valve type, speed of valve turn, fused silica type and ¯ow rate. The novel combination of carry-over and box plot depictions here allows the carry-over distribution characteristic to each FIA-MS system to be rigorously described. This is turn allows a safe approach to packing samples closer together in a liquid stream, when reducing the duty measurement sequence and should be a useful approach for analytical characterisation of high-throughput FIA-MS systems. Acknowledgements We are grateful to Dr. R. Giger and Dr. J. Nozulak of Novartis Pharma, for providing combinatorial chemistry samples to illustrate inter-sample carry-over.
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[11] L.C.E. Taylor, R.L. Johnson, R. Raso, J. Am. Soc. Mass Spectrom. 6 (1995) 387. [12] G. Tang, B.A. Andrien, G.G. Dolnikowski, R.M. Russel, Methods Enzymol. 282 (1997) 140. [13] M.J. Hayward, J.T. Snodgrass, M.L. Thomson, Rapid. Commun. Mass Spectrom. 7 (1993) 85. [14] H.F. SchroÈder, J. Chromatogr. 643 (1993) 145. [15] R.B. Geerdink, P.G.M. Kienhuis, U.A.T. Brinkman, Chromatographia 39 (1994) 311. [16] T. Wang, L. Zeng, T. Strader, L. Burton, D.B. Kassel, Rapid Commun. Mass Spectrom. 12 (1998) 1123. [17] J.N. Kyranos, J.C. Hogan, Anal. Chem. 70 (1998) 389A. [18] S.K. Chowdhury, V. Katta, B.T. Chait, Rapid Commun. Mass Spectrom. 4 (1990) 81. [19] D.J. Douglas, J.B. French, J. Am. Soc. Mass Spectrom. 3 (1992) 398. [20] I. Stewart, Scientific Am. 279 (1998) 11. [21] R. McGill, F.W. Tukey, W.A. Larsen, Am. Statist. 32 (1978) 12. [22] M. Frigge, D.C. Hoaglin, B. Iglewicz, Am. Statist. 43 (1989) 50.
Sorting measurement queues to speed up the ¯ow injection analysis mass spectrometry of combinatorial chemistry syntheses Ramsay Richmond*, Ekkehard GoÈrlach Core Technology Area Analytics unit, Novartis Pharma AG, CH-4002 Basel, Switzerland Received 4 January 1999; received in revised form 12 March 1999; accepted 13 March 1999
Abstract A high-throughput ¯ow injection analysis mass spectrometry system was developed for the purity estimation of multiple parallel combinatorial chemistry synthetic samples. Surreptitious inter-sample carry-over represents a threat to the accuracy of the purity estimates. The visualisation of carry-over in 96-well racks via a surveillance program was improved by introducing the automatic sorting of the intra-rack sample measurement queues. This was necessary as the logical arraying of building blocks by synthetic dispensing robots imparts an underlying row and/or column order to the 96-well racks, which complicates the estimation of carry-over. In a primary sorting step, the molecular weight difference between the expected synthetic products in consecutively measured wells was maximised; in a secondary sorting step, the consecutive measurement of wells with similar building blocks was minimised. Over four hundred samples drawn equally from ®ve diverse combinatorial synthetic families were measured to explore the use of this two-stage sorting to ease the measurement of carry-over, and so to facilitate the optimisation of the ¯ow injection analysis measurement duty cycle. Subsequently the duty cycle in daily use, of 168 s was reduced to 70 s, while maintaining the median inter-sample carry-over at levels below 1%. # 1999 Elsevier Science B.V. All rights reserved. Keywords: Combinatorial chemistry; Flow injection analysis; Mass spectrometry; Optimisation; Measurement sequence; Duty cycle
1. Introduction In combinatorial chemistry two strategies are available to synthesise a library of diverse compounds for bioassay screening: split and mix synthesis, producing mixtures and multiple parallel synthesis, generally producing single compounds. Three years ago, the purity estimation of large numbers of the latter started to confront the Novartis Pharma MS group. We adopted ¯ow injection analysis (FIA) combined with *Corresponding author. Tel.: +41-61-324-6756; fax: +41-61324-5930; e-mail: [email protected]
mass spectrometry (MS) as a starting point [1,2]. Commercial FIA-MS systems with bundled automatic purity estimation software were not then available and so this obliged the in-house development of a highthroughput FIA-MS system along with a networked results reporting system. Electrospray (ESI) was chosen as the ionisation mode for purity estimation of spectra against expected molecular monoisotopic weights. ESI adduction is preferable to the risk of unanticipated fragmentation induced by atmospheric pressure chemical ionisation (APcI), as the large sample numbers effectively prevent comprehensive manual inspection of the spectra.
0003-2670/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved. PII: S 0 0 0 3 - 2 6 7 0 ( 9 9 ) 0 0 2 5 6 - 1
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Our goal was to deliver these purity estimates in a quick and clear way to the spread out combinatorial chemistry customers, via their networked PCs. Mass spectrometry analysis of the customer's 96well rack samples generates an excessive wealth of chemical information. To reduce this to manageable proportions, colour rendering of the data within the Visual Basic RackViewer interface program is used, with numerical purity estimates relegated to the (accessible) background [1,2]. An intrinsic problem of estimating the purities of very large numbers of samples in high-throughput FIA-MS systems is that the operator's familiarity with individual measurements no longer exists. Inter-sample carry-over can therefore appear and escape detection if automatic surveillance systems are not implemented, where inter-sample carry-over is speci®cally de®ned as ion current in a spectrum that can be attributed to the preceding sample in the measurement sequence [3]. There are many reports in the literature describing FIA linked to thermospray, ESI and APcI [4±17]. Optimisation of the MS [14,15] and FIA parts [16] has been reported. Interestingly, inter-sample carry-over does not appear to have been discussed or numerically reported in the FIA-MS literature. Faced with estimating the purities of large numbers of combinatorial chemistry samples, lacking both a priori view of their analytical handling characteristics and operating in a scienti®c ®eld with no previous published literature, forced us to develop our own inter-sample carry-over measurement tools. This involved a novel colourised visualisation of carry-over within RackViewer, for the automated quality control surveillance of large numbers of 96well racks [3]. In this report, the visualisation of carry-over in 96well racks was improved by introducing the automatic sorting of the intra-rack sample measurement queues. This was necessary as the logical arraying of building blocks by the combinatorial chemistry dispensing robots imparts an underlying row and/or column order to the 96-well racks, which complicates the estimation of carry-over e.g. the overlapping of quasi-molecular isotope clusters in consecutive measurements. A primary sorting was developed to maximise the molecular weight difference between the expected synthetic products in consecutively measured wells and a secondary sorting to minimise the consecutive
measurement of wells with similar building blocks. In a pilot study, ®ve different combinatorial chemistries from ®ve different laboratories (racks 165, 275, 508, 700 and 998), chosen for their comprehensive coverage of the analytical handling characteristics of the corporate multiple parallel synthesis programme, were examined. Four sets of FIA-MS conditions were applied, i.e. ESI with a slow duty cycle of 168 s and non-sorted queues, secondly ESI with a slow duty cycle of 168 s and sorted queues, thirdly ESI with a fast duty cycle of 100 s and sorted queues and lastly ESI with very fast duty cycle of 70 s and sorted queues. These are designated as FIA-MS systems A1, A3, A4 and A5, respectively. The ESI with the slow duty cycle and non-sorted queues, i.e. A1 has been our default method in daily use for the last two years [1±3]. 2. Experimental 2.1. Mass spectrometry A Finnigan MAT SSQ-7000 single quadrupole mass spectrometer (San Jose, USA) was used in these experiments, controlled by a DEC AXP 3000/300 workstation under the OSF/1 V3.2D operating system running the Finnigan Interactive Chemical Information System (ICIS) V8.2.1 application software. Two Ethernet-interfaces connected the workstation to a SSQ-7000 and the Novartis local area network (LAN), respectively. A dedicated ®le service accessible by the synthetic chemists was set up on the Novartis LAN, to exchange ®les with that area via TCP/IP (Samba). The Finnigan ICIS package controls a Hewlett Packard 1090 series liquid chromatograph through a HP-IB interface and the autosampler via the RS-232C interface. An autosampler with capacity for eight 96well racks was used (model HTS PAL from CTC Analytics, CH-4222 Zwingen, Switzerland). The maximum overhead gantry speed of the autosampler is 30 cm/s. The CTC installation software, v1.3.135 enabled the six ports Valco valve inject/load orientation to be veri®ed prior to every injection. The MS operating conditions in ESI were: positive ion mode; heated capillary temperature, 2208C; conversion dynode at 15 kV; electron multiplier at 0.9 kV,
R. Richmond, E. GoÈrlach / Analytica Chimica Acta 394 (1999) 33±42
a collision induced dissociation offset of 0.0 V and a spray voltage 4.5 kV. The FIA solvent was CH3CN:H2O 70:30 (v/v), with gradient grade acetonitrile and `water for chromatography' from Merck (D-64271, Darmstadt, Germany). The FIA solvent ¯ow rate was 0.05 ml/min. A 55 cm long PEEK tubing of dimensions 0.127 mm i.d.1.588 mm o.d. (Upchurch Scienti®c, part no. 1535) linked via a zero-dead volume union with a 40 cm long ESI fused silica capillary of dimensions 200 mm o.d.100 mm i.d. (Polymicro Technologies, Phoenix, Arizona 85023-1200, part no. TSP100200), joined the autosampler Valco valve to the exit end of the electrospray spray nozzle (Finnigan part no. 70005-20169) [1,2]. The head pressure for the ESI source nitrogen sheath gas was 413 kPa. The Finnigan API interface version [18,19] is characterised by the dimensions of the source heated capillary, i.e. 0.406 mm i.d.1.588 mm o.d. (Finnigan part no. 70005-98038). A 0.001 mg/ml solution of yohimbine hydrochloride in CH3CN:H2O 70:30 (v/v), infused at 0.05 ml/min was used to tune in ESI. A scan range of 160±1560 m/z per 2 s was used, with centroid data acquisition. This range is wide enough to include any gas dimers for purity estimation. This range avoids three occasional but highly ESI responding contaminants, i.e. [DMSO gas dimerH], [triethylamineH], [triethylamine HCH3CN] and [N-ethyldiisopropylamineH]. 2.2. Rack delivery and preparation The 96-well sample racks arrive for MS analysis, logically arrayed [3,17] by row and column according to their building blocks. The samples are usually of solid-phase origin, and in a relatively high concentration. Parallel to the physical delivery of the racks, Microsoft Excel ®les describing each rack are deposited on a central Microsoft NT server by the combinatorial chemists. The Excel ®les are then called over the network by the MS operator and UNIX script ®les then automatically build the mass spectrometer's analysis page methods; also included is the automatic set up of the MS parameters. This includes the individual synthetic chemist's name, rack's name, desired ionisation mode, location of occupied wells with their formulae and calculation of expected monoisotopic quasi-molecular and adduct m/z positions. The 96-
35
well racks are usually Micronic1Blueline racks from Micronic BV, PO Box 604, 8211 Ap Lelystad, The Netherlands. 2.3. Data handling The two-stage sorting program [20] for the measurement sequence was written in Visual Basic for Microsoft Excel (v5.0c), to process the sample submitting chemist's Excel CSV format ®le that accompanies each 96-well rack. The median values of the carry-over populations for the FIA-MS systems were calculated using SYSTAT v7.0.1 for Windows from SPSS, 444 North Michigan Avenue, Chicago, IL 60611. The box plots [21,22] were plotted using SYSTAT; the inner and outer fence de®nitions for the box plot speci®cally use the recommendations in [21]. 3. Results 3.1. Primary sort Ion current included in the carry-over calculations can originate from at least seven ion current categories; (I) real carry-over from the previous sample; (IIa) artefactual carry-over due to completely overlapping, i.e. isomeric quasi-molecular isotope clusters in consecutive wells; (IIb) artefactual carry-over due to partially overlapping quasi-molecular isotope clusters in consecutive wells; (IIc) artefactual carry-over due to overlapping adduct isotope clusters in consecutive wells; (IId) artefactual carry-over due to fragmentation to identical m/z ions in consecutive wells; (IIe) artefactual carry-over due to quasi-molecular or adduct ions overlapping with fragment ions in consecutive wells (or vice-versa); (III) artefactual carryover due to trace levels of high ionisation responders creeping into the end-product solution, e.g. triethylamine. In the upper screen-shot in Fig. 1, columns 3, 6, and 12 show artefactual carry-over due to inclusion of ion current categories IIb, IIa and IIe, respectively. In the upper screen-shot in Fig. 2, the vertical battlement pattern is due to ion category IIa. All of these are excluded after the two-stage sorting of the measurement sequence, i.e. the lower screen-shots in Figs. 1 and 2.
36 R. Richmond, E. GoÈrlach / Analytica Chimica Acta 394 (1999) 33±42 Fig. 1. The upper screenshot is a RackViewer/Overview of automatic fitted % carry-over for rack 508, using ESI with the slow duty cycle (A1) and non-sorted queues, as in Fig. 3. The colourisation criteria are red for 20%, amber for 5 to <20%, light green for 2 to <5% and dark green <2% carry-over. The lower screenshot is a RackViewer/Overview of automatic fitted % carry-over for rack 508 using ESI with the very fast duty cycle and sorted queues (A5), as in Fig. 5. This has the highest carry-over in column 5, Table 2. The first well measured in each rack has conventionally no carry-over estimate and is coloured white.
R. Richmond, E. GoÈrlach / Analytica Chimica Acta 394 (1999) 33±42 Fig. 2. The upper screenshot is a RackViewer/Overview of automatic fitted % carry-over for rack 700, using ESI with the slow duty cycle (A1) and non-sorted queues, as in Fig. 3. The lower screenshot is a RackViewer/Overview of automatic fitted % carry-over for rack 700 using ESI with the very fast duty cycle and sorted queues (A5), as in Fig. 5. This has the lowest carry-over in column 5, Table 2. The first well measured in each rack has conventionally no carry-over estimate and is coloured white. 37
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38
The default intra-rack measurement queue over the last two years, uses a right to left and top to bottom sequence, i.e. wells B1±B12, C1±C12,. . .H1±H12. The majority of 96-well racks facing us have logical arraying of the synthetic building blocks, giving an underlying row and/or column order. This is clearly unsuitable for optimal estimation of inter-sample carry-over, as there is a risk of categories IIa, IIb, IId, IIe being included in the carry-over calculations. From experimental experience, the risk is also run of including ion current from category III. A recent report [16] describes ultra-high throughput FIA-MS and the simultaneous loading of entire eight well columns from 96-well racks onto a multi-loop injector. This seems ill-conditioned for measuring carry-over, assuming an underlying building block order, i.e. logical arraying by row and/or column. Therefore the default measurement sequence B1± H12 was sorted in ascending order on the basis of molecular weight. This is considered the primary sorting and was done within Microsoft Excel, using the customer submitted Excel CSV format input ®le. The ®rst half of the sorted list was then rif¯e shuf¯ed [20], with the second half of the sorted list, i.e. ®rst well of ®rst half paired with ®rst well of second half, second well of ®rst half paired with second well of second half and so on. This maximises the molecular weight difference between consecutively measured wells for the entire rack. Therefore the quasi-molecular ion regions in consecutive wells will be as far apart as possible and this should minimise the risk of including ion current categories IIa, IIb and IIc.
However consecutive wells can occasionally have the same row or column after this sort, e.g. wells B12, F12 or G5, G12, see third column, Table 1. This runs the risk of ion current categories IId and III being included in carry-over calculations. Therefore a secondary sorting was developed. If a row or column similarity is found in any consecutive pairs in the measurement sequence, the culprit well, deemed as the second in the consecutive pair, was exchanged with the well two ahead in the measurement sequence. If however this exchange simply recreates this row or column similarity event, then the exchange is reset, and successive attempts are made with wells four, six and eight ahead. If this fails at the fourth try, then the sorting proceeds to the next row or column similarity event. Once the forward similarity sorting is ®nished, an identical sorting in the reverse direction is made, see Table 1. Columns 8 and 9 in Table 1 show the necessity of the reverse sorting to eliminate all occurrences of row or column similarity in consecutively measured wells. For ease of comparison, box plots rather than colourised RackViewer/Overviews are used to depict the carry-over distributions for the four FIA-MS systems A1, A3, A4 and A5. The carry-over data was processed using a 4 a.m.u. ®lter for consecutive well molecular weights to eliminate artefactual ion current. The carry-over distributions in the four FIA-MS systems are highly skewed and contain outliers. Box plots are insensitive to outliers and display the following features of a distribution; median, spread, skewness, tail length and outliers. Also, since the median is
Table 1 Indices for five different racks at the initial, two intermediate steps and end of two-stage sortinga Rack
165 275 508 700 998 a
Primary sorting cumulative molecular weight differences for the entire rack i.e. (|mw|)
Secondary sorting row or column similarity events
Initial
After mw sort
After forward sort
After reverse sort
Initial
After mw sort
After forward sort
After reverse sort
2900 5163 4222 1466 2066
5319 12030 7880 3969 4406
5326 12040 7880 3969 4406
5326 12040 7880 3969 4406
77 77 77 77 77
4 19 14 17 15
1 0 0 4 2
0 0 0 0 0
The goal of the sorting is the minimisation of row and column similarity and the maximisation of molecular weight difference in consecutive measurements. The sorting sequence in time is from left to right. The final indices are in the fifth and the ninth columns. The majority of changes occur in the first molecular weight sorting.
R. Richmond, E. GoÈrlach / Analytica Chimica Acta 394 (1999) 33±42
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Table 2 Median inter-sample % carry-overs for ESI with slow duty cycle and non-sorted queues (A1); ESI with slow duty cycle and sorted queues (A3); ESI with fast duty cycle and sorted queues (A4); ESI with very fast duty cycle and sorted queues (A5) Rack
FIA-MS system A1 median % carry-over
FIA-MS system A3 median % carry-over
FIA-MS system A4 median % carry-over
FIA-MS system A5 median % carry-over
165 275 508 700 998 Pooled
0.82, 0.16, 0.91, 1.10, 1.07, 0.74,
0.36, 0.07, 0.07, 0.02, 0.00, 0.06,
0.30, 0.05, 0.11, 0.04, 0.00, 0.06,
0.18, 0.10, 0.36, 0.00, 0.00, 0.01,
n69 n83 n69 n48 n48 n317
n83 n83 n83 n82 n83 n414
n83 n83 n83 n82 n83 n414
n83 n83 n83 n82 n83 n414
A 4 a.m.u. filter for consecutive well molecular weights was used to eliminate ion categories IIb and IIc in all four systems. The FIA-MS system A1 is the `ESI with intervening washes' described in [3] and used as the default system in [1,2].
relatively insensitive to outliers compared to the mean, the former was used here as the de®ning parameter for carry-over. In Table 2, there is a clear reduction in the median inter-sample carry-over of all ®ve racks, going from using non-sorted measurement queues of FIA-MS system A1 (0.74%) to the sorted queues of system A3 (0.06%). But there is no clear difference in the median carry-over, going from the system A3 slow duty cycle of Fig. 3, to the system A4 fast duty cycle (0.06%) of Fig. 4, and then to the system A5 very fast duty cycle (0.01%) of Fig. 5. Therefore the last was adopted as our new default FIA-MS system for daily
use. The relative standard deviation of the carry-over estimates derived from measurements (n5) of the entire column 6 in rack 508 (Fig. 1) by FIA-MS system A1, is 9.46%. The relative standard deviation of the purity estimates is 7.42%, derived from measurements (n10) of the entire column 6 in rack 508 [1±3]. 3.2. Optimisation of the FIA-MS measurement duty cycle In the slow FIA-MS duty cycle used in [1,2] a major consideration was that the wash injection in the FIA
Fig. 3. Slow FIA-MS duty cycle with injection variation as in A1 and A3. Sample and wash injections are controlled from the MS and loop washes from the autosampler. Flow rate50 mls/min and peak elutes at 20 s. The duty cycle of 168 s is punctuated as follows: (1a) sample acquisition start; (2a) sample acquisition stop; (3a) wash acquisition start; (4a) wash acquisition stop; (5a) next sample acquisition start.
40
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Fig. 4. Fast FIA-MS duty cycle with injection variation as in A4. Sample and wash injections are controlled from the MS and loop washes from the autosampler. Flow rate50 mls/min and peak elutes at 20 s. The duty cycle of 100 s is punctuated as follows: (1a) sample acquisition start; (2a) sample acquisition stop; (3a) next sample acquisition start.
stream should be acquired as a data ®le. The logic was that it provided an alternative approach for measuring carry-over risk compared to the direct ®tted method in [3]. The total ion current of the spectrum from this
wash FIA pulse could be measured and compared with that of the next sample. The resultant ratio should provide an estimate of the maximum carry-over risk. An experimental survey of the ®ve different combi-
Fig. 5. Very fast FIA-MS duty cycle with respective injection variation as in A5. Sample and wash injections are controlled from the MS and loop washes from the autosampler. Flow rate50 mls/min and peak elutes at 20 s. The duty cycle of 70 s is punctuated as follows: (1a) sample acquisition start; (2a) sample acquisition stop; (3a) next sample acquisition start.
R. Richmond, E. GoÈrlach / Analytica Chimica Acta 394 (1999) 33±42
natorial chemistries, using a RackViewer visualisation window depicting this TICwash/TICsample ratio, showed this approach's clear failings. Speci®cally this `extrapolative maximum carry-over risk' strategy grossly exaggerated the real carry-over, probably due to non-linearity in the ESI dynamic response curves for the analytes involved. Once this was realised, the necessity of data acquisition of the intervening washes fell away. However the experiments in [3] demonstrated the apparent necessity of at least some of the physical elements of the wash injection sequence in Fig. 3, to maintain the median intersample carry-over at <1%. Therefore the wash injection analyte pulse resulting from the valve wash, was positioned into a non-acquisition zone immediately after the sample acquisition (delay no. 1, Fig. 4). This allowed the duty cycle to be initially reduced from 168 to 100 s. Experimentation to further reduce the measurement duty cycle, involved the elimination of this second injector valve switching into the solvent stream, i.e. FIA-MS system A5, while retaining the second loop wash (Fig. 5). This allowed a further reduction of the duty cycle to 70 s, while maintaining inter-sample carry-over at levels below 1%, see median cross bars in Fig. 6.
Fig. 6. Box plots of automatic fitted carry-over for FIA-MS systems A1, A3, A4 and A5, pooled for all five 96-well racks. The cross-bar with the box is the median; upper and lower box edges enclose 50% of distribution. The median cross-bar in A5 has visually merged with the lower box edge. Asterixes and circles represent outliers and far outliers, respectively [21,22]. Only consecutive wells with a | molecular weight| >4 a.m.u. were used, to exclude grossly artefactual ion current.
41
The autosampler used in this study has an overhead gantry containing the sampling syringe. This architecture has certain implicit time imperatives, especially evident in the very fast duty cycle (Fig. 5). The syringe's resting position to 96-well rack and then to injector involves a gantry movement taking 28 s. The delay for a subsequent loop wash involves another movement needing 17 s. Therefore in the very fast duty cycle, (Fig. 5), the autosampler's mechanical movement involves a minimum of 62 s, occupying 89% of the duty cycle. The residual 11% is used for positional calibration of the autosampler's injector head. 4. Conclusions Logical arraying of building blocks by combinatorial chemistry dispensing robots complicates the accurate estimation of inter-sample carry-over in combinatorial chemistry racks. This in turn hinders the optimisation of the FIA-MS measurement duty cycle. A novel visualisation program for monitoring surreptitious inter-sample carry-over combined with a two-stage sorting of intra-rack measurement queues, facilitated an incremental decrease in the FIA-MS measurement duty cycle, from 168 to 100 s and then to 70 s. The median inter-sample carry-over was maintained at less than 1%. FIA-MS systems are comprised of many physical components interacting in a speci®ed time sequence, e.g. loop type and size, number of loop washes, valve type, speed of valve turn, fused silica type and ¯ow rate. The novel combination of carry-over and box plot depictions here allows the carry-over distribution characteristic to each FIA-MS system to be rigorously described. This is turn allows a safe approach to packing samples closer together in a liquid stream, when reducing the duty measurement sequence and should be a useful approach for analytical characterisation of high-throughput FIA-MS systems. Acknowledgements We are grateful to Dr. R. Giger and Dr. J. Nozulak of Novartis Pharma, for providing combinatorial chemistry samples to illustrate inter-sample carry-over.
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