non combinatorial chemistry

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▼ The launch of combinatorial chemistryonto an unsuspecting pharmaceutical indus-try in the early 1990s resulted in several fran-tic efforts as companies tried to maintain acompetitive edge through the generation andscreening of compounds in unprecedentednumbers and at an unprecedented rate. Thefocus at that time was solid-phase synthesisand the generation of compound mixtures,resulting in library arrays of many millions ofcompounds. These combinatorial libraries re-quired considerable analytical detective workto ascertain whether the desired compoundshad been made. Frequently, the library ap-proaches were frustrated by restrictive solid-phase chemistry of limited success or general-ity. Furthermore, the observation of biologicalactivity in assays of mixtures frequently re-sulted in a wild-goose chase that employedsophisticated or tedious deconvolution or decoding, pursuing hits that often vanished:the false-positive problem. Having sufferedseveral resource-intensive yet fruitless pur-suits, in the mid- to late-1990s, many compa-nies were forced to radically rethink their

combinatorial strategies1–4. There was nodoubt that the new technology and the readyavailability of automated synthesis offeredopportunities for increased productivity inboth file enrichment and lead optimization.However, it was a certainty that chemistrywould not permit easy success when therewas such a casual disregard of the basic tenetsof good science – careful experimental plan-ning and meticulous observation of results.Indeed, we commented in 1995 that in the fu-ture of drug discovery through combinatorialchemistry, ‘quality, rather than quantity, willbecome the new goal’5.

Thus, library synthesis reverted towards theold values of quality, both in compound design and product analysis. Although thisinevitably resulted in fewer compounds beingprepared overall, we have now reached a stagewhere those that are made are the products ofmore considered design, meticulous protocoldevelopment and thorough structural analy-sis. Medicinal chemists who exploit high-speedchemical techniques are increasingly makingsingle compounds, depending primarily onthe proven legacy of solution-phase chem-istry. The products are often purified, or sub-jected to rigorous analysis, before being sentfor registration and screening.

We have now reached a phase in the devel-opment and application of high-speed chem-istry that permits library compounds that areactive in biological screens to be identifiedwithout deconvolution, fractionation or tagdecoding, and, more significantly, there aremuch fewer false-positives. Because the prep-aration of targeted single compounds removesthe requirement for the synthesis of combi-natorial arrays, we refer to our approach asbeing ‘non-combinatorial’.

The application of non-combinatorialchemistry to lead discoveryJeremy Everett, Mark Gardner, Frank Pullen, Graham F. Smith, Mike Snarey and Nick Terrett

*Jeremy EverettMark Gardner

Frank PullenGraham F. Smith

Mike SnareyNick Terrett

Medicinal TechnologiesPfizer Global R&D

SandwichKent, UK

CT13 9NJ*tel: +44 (0)1304 616161fax: +44 (0)1304 656259

e-mail: [email protected]

reviewsresearch focus

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DDT Vol. 6, No. 15 August 2001

Non-combinatorial chemistry is a powerful technology for the synthesis

of large numbers of compounds, with complete control over the properties

of those compounds. We have developed a Library Creation, Registration

and Automation system (LiCRA), which harnesses an efficient non-

combinatorial chemistry design and synthesis engine, together with

high-throughput automated purification. This LiCRA system also operates

in a closed loop mode for hit-to-lead optimization, and contains an

integrated IT system that controls and facilitates all aspects of the

operation from design to registration. Quality has been our watchword,

from the quality of compound design through to the quality of the

products.

This article describes the non-combinatorial approachdeveloped and used by our Library Design and ProductionGroup, and demonstrates that a combination of rationaldesign, careful synthesis and compound purification be-fore testing can generate high quality biological data thatresult in novel leads and new drug discovery programmes.

Non-combinatorial chemistryNon-combinatorial chemistry is a phrase coined to expressthe difference in strategy between our technique and com-binatorial chemistry as it is more commonly applied.Combinatorial chemistry involves the use of all combi-nations of a set of reagents to produce an ordered array ofproducts. For example, a combinatorial amide library syn-thesis starting with 80 amines and 60 acids would make4800 amide products (80 × 60). In non-combinatorialchemistry, the same reagent sets can be used, but the prod-ucts would be individually chosen for synthesis, such thatthe 80 amines and 60 acids might be used to select anynumber of products from 80 up to the combinatorial limitof 4800, and still use each reagent. This approach is alsocalled ‘cherry-picking’ by some groups. The key differentialis to allow the design, synthesis, purification and screeningof any group of individual compounds from a virtual library, not just arrays of compounds. The drive for thisstrategy is the desire to exploit virtual libraries that are aslarge as possible in a powerful and flexible manner, whileretaining total control over the properties of each product.The approach is important in both file enrichment andlead optimization.

Application of non-combinatorial chemistry to fileenrichmentNon-combinatorial chemistry is important in file enrich-ment in that it allows the synthesis of diverse sets of com-pounds, using as many monomers as possible, not just asmall subset. The reason why this is important is best illustrated by an example. Consider a three component library, represented as A–B–C. Let us assume that A areamines, B are amino acids and C are acids, so the productswill be diamides. It can be easy to acquire 500 amines, 100amino acids and 500 carboxylic acids from commercialsources, giving a virtual library size of 25 million compounds. Clearly, it is unlikely that 25 million singlecompounds will be synthesized using parallel synthesis.Therefore, a choice has to be made as to how to exemplifythis virtual library (with perhaps 8000 examples) in a compound collection for screening purposes. A typical approach might be to select 20 amines, 20 amino acids and20 carboxylic acids to give a combinatorial array of 8000examples. Frequently, diversity-based methods might be

used to select the representative building blocks. However,this is an approach with significant drawbacks; a typicalsituation that might be faced in selecting 20 amino acidsfrom a set of 100 is presented in Fig. 1.

As a subset of the whole exercise of choosing 20 from100, two amino acids might have to be chosen from theset of six presented in Fig. 1. If you have been involved in such an exercise, you will recognize the similarity of this situation with examples from your own experience.Although software packages might be able to make a selection, the truth is that there is no correct way tomakethis choice for a file enrichment library. Whicheverpair you choose, they clearly cannot adequately representall six. Once you have made your selection of a pair ofamino acids, remember that in this library of 8000 com-pounds, each of those amino acids will be present in 400products (in combination with the 20 selected amines and20 selected carboxylic acids). The other four amino acidswill be present in no products at all.

An alternative approach is to construct the 8000 compounds in a non-combinatorial manner, using all the available building blocks. In this example, if each of thecomponents A, B and C is used equally, each amine andcarboxylic acid would be present in 16 (8000/500) prod-ucts, and each amino acid in 80 (8,000/100) products.Arbitrary choices of monomers no longer need to be made– all can be used in the file enrichment library – thus pro-viding much greater diversity of products. The preparationfor synthesis of such a library does take longer than for acombinatorial array (it takes longer to weigh and solubilize1100 components than 60). In addition, automation isnow essential for synthesis because neat arrays that couldbe handled manually are no longer being used.

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DDT Vol. 6, No. 15 August 2001reviews research focus

Figure 1. Six amino acids. Your task is to choose the two mostdiverse amino acids that should be selected as monomers forfile enrichment. This is representative of the problem faced incombinatorial file enrichment, in which a small number ofmonomers have to be selected in an attempt to represent theproperties of a much larger set.

H2NOH

O

H2NOH

O

H2NOH

O

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O

H2NH

O

OH

O

H2N O

Drug Discovery Today

A further significant benefit of thenon-combinatorial approach is thatyou can choose not to synthesize spe-cific products, and thus precisely tailorthe physicochemical properties of thenon-combinatorial set. For example, itis straightforward to calculate the prop-erties of the products and to ensurethat all of those to be synthesizedshould comply with certain distribu-tions of molecular weight or clogP values, Lipinski’s rule of five6 or otherdesired properties. An example of theeffect of this flexibility can be seen inFig. 2.

The standard combinatorial arraywould give something approaching anormal distribution of a property suchas molecular weight. However, withnon-combinatorial synthesis, you cansynthesize only those products withmolecular weights below a given valueor in a given value-range. Our in-houselibrary design software, S1D (selectionin one dimension, i.e. by additive or approximately additive fragmentalproperties, such as molecular weight orclogP) allows for the application ofthese design principles and has beenused to select diverse libraries of specific design for file enrichment for three years. S1D makes pseudo-random selections of products from a virtual library in a way thatattempts to use each monomer an equal number of times,but with boundary conditions such that the calculatedproperties of each product (molecular weight, clogP, and so on) fall within predetermined limits. S1D links to theLibrary Creation, Registration and Automation system(LiCRA). Both LiCRA and S1D were written by thechemoinformatics and Discovery Research informaticsgroups in Pfizer Global R&D, Sandwich, UK.

Application of non-combinatorial chemistry to leadoptimizationAlthough non-combinatorial synthesis offers significantbenefits in file enrichment, the strategy is particularly pow-erful once there are some design principles to apply, suchas when an active compound or a crystal structure is inhand. Non-combinatorial synthesis gives the medicinalchemist complete freedom to choose any selection of single compounds from a given virtual library to expandupon this structural information. Virtual libraries can also

be searched by computational techniques, such as pharma-cophore searching or crystal structure ‘docking’, and thehit-lists synthesized as they are or manipulated further.

Non-combinatorial synthesis using LiCRAOur first significant problem was that at the time westarted, nobody had considered that non-combinatorialsynthesis would ever be a user requirement of ‘combina-torial chemists’, and still many do not. We were drawn to our solution by the knowledge that our HTS groups used‘cherry-picking’ software on Tecan liquid handling robots(Tecan UK, Reading, UK) to choose active compounds fromHTS screening plates. However, no software was availableat that time for our library synthesis task, because of ourunique in-house requirements, and we decided, therefore,to design and build LiCRA ourselves.

When our group started work in 1997, we used eachpiece of automation in isolation to try to understand whatinputs and outputs were important for us to monitor andchange. In general, most commercial automation runs onits own software with minimal opportunities for broaderintegration. A flat file can often be read to load sample data

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Figure 2. An illustration contrasting property distribution in a combinatorial selectionwith that in a non-combinatorial selection. Well-defined property cut-offs can be readilyachieved via non-combinatorial approaches.


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and a further file is often written to the workstation to logautomation activity. Our first attempt at synthesis softwaresimply read flat files from a shared network drive and pro-duced an input file for the next step of the process. Thisproved successful and was flexible in the event of machineerrors. User intervention and file manipulation, however,was causing problems with tracking, and serious humanerrors were increasing.

The latest version of LiCRA is based around an Oracledatabase (Oracle UK, Reading, UK) of the individual com-pound batches that we have chosen to make. Their non-combinatorial selection means that, compared with arraysynthesis, there is much less common data from one batchin a library to the next. Our main reasons for moving to adatabase-centred system were to increase storage and secu-rity and to facilitate data analysis and trend recognition.Compliance with data storage and retrieval requirementsfor patent purposes made us look into this more robust approach.

The LiCRA user deals with a Visual Basic interface, whichtreats all the compounds in a library together (e.g. reactiontemperature, molar equivalents) but tracks most other dataat the individual compound level (e.g. yield, purity andprotecting groups). A simple input interface that requiresthe product structure in MDL (Molecular Design Ltd,Camberley, UK) Structure definition (SD) file format, and an ASCII (American Standard Code for InformationInterchange) file containing compound numbers with con-stituent monomers, is all that is required to load a library.Typical libraries range from 200 to 2000 compounds. Inplanning a synthetic scheme, volumes, equivalents and soon, can be entered for a whole library but can also be altered on a monomer-by-monomer basis. An automaticreservation can be made against the monomer inventorysystem and lists of quantities printed if required. Monomersthat appear in the library design but are not present at all,or are in insufficient quantity in the monomer store, areflagged for intervention. The LiCRA inventory control system interfaces directly with accurate balances duringweighing and calculates the exact amount of each monomerweighed. The weighed monomer samples are bar-codedand loaded into racks for synthesis. LiCRA produces a re-port that warns when monomers are below a predeterminedre-order level, so that the inventory can be maintained.

The LiCRA synthesis system uses several standard androbust pieces of commercial hardware. All solution dis-pensing activities are performed by Tecan Genesis liquidhandling robots, which have a proprietary interface fornon-combinatorial sample distributions. Specific racks andboxes have been custom-made to enable inert atmosphere,corrosive reagent and solid-phase chemistry capabilities.

All chemical incubations are performed off-line in simpleshakers or ovens, and all products are sent through our in-house autopurification system (see later). Samples arereformatted into 96-well Micronics plates (Micronics,Redmond, WA, USA) for registration that allows a finalquantitation by weighing to be performed using a Bohdan

Weighing Station (Mettler-Toledo Myriad, Royston, UK).The user is then presented with quality-control data on aplate-by-plate basis to allow review of spectra and individualsample data before registration.

LiCRA has built-in links to several chemical structuredatabases to allow easy analysis of monomer solubility, re-activity and other properties. Product structures are alsostored within the LiCRA database to allow more chemicallyintuitive quality control. Because all synthesis informationis stored within the database, trends in solubility and reac-tivity of individual monomers can be tracked to enhancethe quality of future libraries. When the chemist is satis-fied with the library products, a structure definition file,which contains all relevant information, is produced andsent to our compound control centre for registration to thecorporate database.

Automated purification of compounds from non-combinatorial synthesisThe non-combinatorial chemistry approach to file enrich-ment and lead optimization, as described earlier, generateslarge numbers of single compounds, all of which need tobe purified to minimize the presence of impurities and thegeneration of false-positives in biological screens. Analysis,purification and quantitation of large numbers of com-pounds in 96-well plate format has been viewed by manyas a highly labour intensive process. Systems are availablefor high-throughput purification (e.g. the Biotage ParallexFlex™ system – Dyax, Charlottesville, VA, USA). However,there are currently no commercial purification systems onthe market that can effectively process, quantify and tracklarge numbers of compounds in an automated way. Thekey to any automated purification system is a fully inte-grated sample tracking process, coupled with proven purification technology and linked to a user-friendly soft-ware interface. A useful review of automated purificationmethodologies and emerging supercritical fluid chro-matography-based methods is available7.

We have developed an autopurification system based ongradient HPLC separation, off-line MS analysis of fractionsand quantification by light scattering that has the capacityto purify up to 100,000 compounds per year at the 10–50 mgscale. The software interface (OmniTrak, developed byAitken Scientific, Thame, UK) is fully integrated withLiCRA, and controls all the HPLC, MS and robotic hardware

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involved in the purification process.There are eight semi-preparative HPLCsystems [Gilson 306 gradient systems,with Gilson 215 liquid handlers config-ured as both autosamplers and fractioncollectors, operating under Unipointcontrol software (Gilson, Middleton,WI, USA)]. The HPLCs all use simplegeneric gradients to effect separationand purification (acetonitrile–0.1% tri-fluoroacetic-acid gradients runningfrom 5–95% organic over 8 min at 4 mlmin–1 with a total run time of 15 min).Each 96-well compound plate has abarcode generated from LiCRA linkedto database records describing the 96expected compounds and assigning anempirical formula to each well. Each ofthe 96 compounds is fractionated bygradient HPLC into bar-coded 48-wellplates, collecting up to five fractionsper compound using UV-triggered frac-tion collection. The 48-well plates aremanually loaded onto a carousel andan ORCA® robot then automaticallytransfers the plate to a Gilson 231 auto-sampler for analysis (Fig. 3). Each fraction well is analyzed by flow injection MS (Micromass® Platform LC, Micromass® UK, Manchester, UK)that operates in parallel with evap-orative light scattering (Sedex Sedere55, Thomson Instrument Company,Chantilly, VA, USA). The MS identifiesthe fraction or fractions with the expected empirical formula for each of the 96 compounds throughout the series of 48-well fraction plates.The evaporative light-scattering data provides an estimate of the amount of compound present in the fraction by reference to a calibration file8. The correctly identified fractions that have sufficient sample mass are thenautomatically selected and compressed into a bar-coded plate of tared 5 mlMicronics tubes to be evaporated todryness (Genevac, Ipswich, UK). Afterautopurification, all the MS and chro-matographic data on any compoundcan be accessed from within LiCRA.

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Figure 3. A view of part of the autopurification system: the linear track ORCA® robotplate-handler (Beckman Coulter, Fullerton, CA, USA) is housed in a recirculating safetycabinet. The MS and evaporative light scattering unit are situated below the bench onthe right-hand side. Some of the eight semi-prep HPLCs can be seen in the background.

Figure 4. (a) A plate view from the LiCRA system, illustrating the connectivity betweenthe 96-well plate of products and all of the associated structural, analytical and syntheticinformation. The colour coding illustrates success (green) or failure (red) of the chemistry at the well level. (b) A typical dual wavelength UV chromatogram of the crudeproduct from a library synthesis. (c) Positive- and negative-ion electrospray MS of thedesired fraction (fraction 61, vial 13) from the purification of this crude product. (d) Atypical 1[H] NMR showing the quality of compounds obtained using this autopurification system.

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Using this technology, library chemists can purify theircompounds without involving themselves in the detailedunderstanding of HPLC or MS, as all the identification,analysis and quantification is carried out in an automatedway. Compounds purified in this high-throughput and automated fashion are of comparable quality with thoseproduced by traditional synthesis and purification technol-ogies; they are typically of 90–95% purity (Fig. 4). All thecompounds are tracked from their synthesis to registrationusing a combination of OmniTrak and LiCRA, and all theanalytical data for a particular compound are linked to thattracking process.

Application of S1D and LiCRA non-combinatorialchemistry to hit-to-lead optimizationWe have applied the S1D and LiCRA systems to several tar-get types. The example that follows illustrates the power ofthe technology to optimize some weak hits coming from akinase screen to generate multiple, potent leads for chem-istry follow-up. Five thousand compounds were selectedusing S1D for non-combinatorial synthesis from a virtuallibrary of 300,000 compounds that was designed specifi-cally for a kinase screen. Figure 5 illustrates the type ofchemistry used in this library subset. Figure 6a shows thatthe only activity observed on screening these compoundscame from three members of a subset of 700 compoundsmade from just two templates: all other templates (4300compounds) were inactive. The three active compoundswere repeatedly tested and had IC50 values of 5–10 µM. Thehits were followed up by non-combinatorial synthesis of aclosed-loop subset of 693 compounds from the same vir-tual library, focussing in on the regions of chemical spaceoccupied by the three original hits and related regions (Fig. 6b). This subset gave a respectable 2.3% hit rate at 10 µM. Furthermore, repeating the screening with solidsamples for some of the more active compounds gave thesame Ki values, reinforcing our confidence in the identityand quality of the parallel synthesis-derived liquid samplesused in the screens.

The early SAR information generated from this firstclosed-loop prompted the synthesis of a second loop of345 compounds, which gave an impressive 66% hit rateagainst the enzyme at 10 µM (Fig. 6c). These compoundshad improved solubility, and exhibited weaker activity andindependent SARs against key kinase-selectivity targets.Another 1400 compounds were made in four succeedingclosed-loops, in the course of which the virtual library wasexpanded from 300,000 to 4,600,000 compounds by theaddition of further custom and commercial monomers. Asummary of the outcome of all six closed-loops is shown inFig. 6d. It is clear from Fig. 6a–d that S1D has been used to

avoid the selection of high molecular weight and lipophiliccompounds that would occupy the top right-hand side ofthe charts, because of the way in which the monomer listswere ordered. The fifth and sixth loops delivered three dif-ferent series of selective and potent compounds with activ-ity against the kinase in the region of 10–50 nM. All of the2500 rule-of-five6 compliant compounds made in the sixclosed-loops were synthesized as singletons, each one ofwhich was analyzed and autopurified as described above;all with less than one ‘person-year’ of effort. The leads wediscovered have provided the basis for a chemistry-staffedlead-optimization project, which is now under way.

ConclusionsWe have demonstrated that non-combinatorial chemistryis an effective method for screening-file enrichment andfor lead optimization. We believe that whatever strategy isused for library chemistry, the quality of design and thequality of the products are of paramount importance; wehave achieved this with our S1D and LiCRA systems. Weintend to build upon this system in the future and add newdesign tools and new synthetic capabilities to expand thescope and quality of the virtual chemistry space accessibleto our chemists.

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Figure 5. The design for some of the diamino-substitutedaromatic heterocycles used in the kinase virtual library. Thislibrary employed simple, solution-phase, chlorine displacementchemistry using large available monomer sets in 96-wellpolytetrafluoroethylene plates.

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2) R3R4NH

R1, R2, R3, R4 = H, lower alkyl, cycloalkyl, substituted alkyl, substituted cycloalkyl, heteroaryl, benzyl

R5, R6 = H, lower alkyl, cycloalkyl, substituted alkyl, substituted cycloalkyl, heteroaryl, benzyl, fused aryl, fused heteroaryl, fused cycloalkyl


AcknowledgementsWe are grateful for the expert assistance provided in hard-ware development, automation, information systems,chemistry and screening by the following Pfizer colleagues:Mark Kemp, John Taylor, Pam Greengrass, Duncan Armourand their teams; Ruth Tucker, Mark Lord, Ian Parsons,Jason Robson, Derek Hearn and Wilma Keighley and herteam; James Merson and his team; and George Perkins,Chris Selway, Mike Stace, Mark Taylor and Adrian Wright.We are also grateful to Andy Kemp of Aitken Scientific, IanHibbard and Grant Cameron of Anachem and Jeremy Battof Micromass for their productive collaboration on the autopurification system, and to David Webb of PA Consultingfor project management of LiCRA and S1D.

References1 Labaudiniere, R.F. (1998) RPR’s approach to high-speed parallel

synthesis for lead generation. Drug Discov. Today 3, 511–5152 Hird, N.W. (1999) Automated synthesis: new tools for the organic

chemist. Drug Discov. Today 4, 265–2743 Dolle, R.E. (2000) Comprehensive survey of combinatorial library

synthesis: 1999. J. Comb. Chem. 2, 383–4334 Coates, W.J. et al. (2000) Successful implementation of automation in

medicinal chemistry. Drug Discov. Today 5, 521–5275 Terrett, N.K. et al. (1995) Combinatorial synthesis – the design of

compound libraries and their application to drug discovery. Tetrahedron 51, 8135–8173

6 Lipinski, C.A. et al. (1997) Experimental and computational approachesto estimate solubility and permeability in drug discovery anddevelopment settings. Adv. Drug Deliv. Rev. 23, 3–25

7 Ripka, W.C. et al. (2001) High-throughput purification of compoundlibraries. Drug Discov. Today 6, 471–477

8 Fang, L. et al. (2000) Evaluation of evaporative light-scattering detectorfor combinatorial library quantitation by reversed phase HPLC.J. Comb. Chem. 2, 254–257

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Figure 6. (a) Non-combinatorial selection of 700 compounds from a virtual library of 300,000 in two templates: yellow and dark-bluediamonds. The three active compounds are circled in red. Each axis of the chart consists of a list of the amine monomers used in eachposition around the aromatic core. The x-axis corresponds to the amines used to displace the most reactive chlorine, the y-axis to theamines used in the displacement of the second, less reactive chlorine. The amines were ordered by similarity (according to a judgementmade by medicinal chemists; see Fig. 7 for design outline). (b) A corresponding view of the 693 compounds made in the first closed loopin response to the three hits found in Fig. 8a. The original 700 compounds screened are represented as green dashes; the 693 newcompounds in the first loop are shown as blue diamonds (inactive) and red circles (active); the three original hits upon which the SAR wasbased are still shown circled in red. In this display, some of the compounds synthesized sit on top of one another in the chart, as multipletemplates sometimes shared the same monomer pairings; this third dimension of the data is missing from this compressed 2D view. (c) Corresponding figure for the second closed-loop; all previous compounds are now shown as green dashes, and the second loopcompounds as blue diamonds (inactive) or red circles (active); again, this is a compressed 2D view of a 3D dataset. (d) A compressed 2Drepresentation of all six closed-loops (CLs). The compounds from the six loops are represented with the following symbols: CL1, yellowtriangles; CL2, magenta squares; CL3, blue diamonds; CL4, brown circles; CL5, open red diamonds; CL6, blue dashes; two groups of filecompounds are shown as crosses and as crossed crosses.

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