6Structural Fragments in Marketed Oral DrugsMichal Vieth and Miles Siegel

6.1Introduction

In this chapter we present a view on the chemically and pharmacologically rele-vant fragments/building blocks of marketed drugs, with particular emphasis onthe fragments contained in oral drugs. Several recent papers have emphasized theevolution of the physical properties of oral drugs [1–3]. Some authors have sug-gested that the evolution of the target and chemistry space has caused small butmeaningful changes in the mean properties of newer drugs as compared witholder drugs [2, 3]. However, we have found little evidence in the literature to linkthese apparent differences to specific chemical fragments that are the pharmaco-phorically important building blocks of drugs. This chapter describes an analysisof the fragments present in drugs and other groups of compounds, to investigatesimilarities and differences between these groups. In particular, we look into thefrequency of occurrence of fragments in groups of compounds, to emphasize andunderstand physical property similarities and differences between sets. By takingthis approach, we have found that the distribution of chemical fragments in inject-able drugs differs significantly from that of oral drugs, mostly in the scaffolds con-necting these fragments. We also use a similar comparison to look into the frag-ment-based similarities between oral drugs and compounds in clinical and precli-nical development. In addition to speculating on the interesting implications ofthese findings, we present some practical directions on how the available data canbe utilized to guide compound development in the direction of increasing prob-ability of pharmacokinetic success.

6.2Historical Look at the Analysis of Structural Fragments of Drugs

Retrosynthetic combinatorial analysis procedure, or RECAP [4], was the first toolused to generate, analyze, and suggest the utilization of a set of pharmacologicallyrelevant fragments from the set of molecules in a drug database. The significance

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Fragment-based Approaches in Drug Discovery. Edited by W. Jahnke and D. A. ErlansonCopyright � 2006 WILEY-VCH Verlag GmbH & Co. KGaA,WeinheimISBN: 3-527-31291-9

of RECAP in comparison to other fragmentation approaches [5, 6] was the almostimmediate and clear-cut utilization of the knowledge present in the database of bio-logically relevant compounds in the design and potential synthesis of compound li-braries. It is worth noting the RECAP approach came before the concept of targetedlibraries [7–9] and inspired a number of approaches for de novo generation of che-mically feasible and pharmacologically relevant structures [10–13]. The visionarycharacter of RECAP can be best described by the number of follow-up approachesand companies funded around the concept, including De Novo Pharmaceuticals(www.denovopharma.com) and Locus Discovery (www.locusdiscovery. com).

Interestingly, the RECAP analysis of the World Drug Index [14] gave signifi-cantly different fragments from our recent analysis of marketed drugs using a si-milar fragmentation approach. For example, of the 15 most common side-chainsin oral and injectable drugs found in our work, shown in Fig. 6.1, only six were ex-emplified in 35 presented using the RECAP method; simple phenyl, the side-chain we found to be most common in both sets of drugs, was not exemplified byRECAP.

114 6 Structural Fragments in Marketed Oral Drugs

Fig. 6.1Comparison of the most frequent side-chains in oral (a) and in-jectable (b) drugs. The numbers indicate the count of the drugscontaining that fragment. The means of properties are not sig-nificantly different for a and b. Reproduced from Vieth at al. [1].

6.3Methodology Used in this Analysis

The structural fragments analyzed in this chapter were generated using an internallydeveloped tool, Molecular Slicer (MS), which we routinely use to analyze the com-mon fragments of biologically targeted compounds [1]. MS is similar to other retro-synthetic algorithms previously described in the literature, such as RECAP [4] andREOS [15]. Our tool decomposes compounds into core and side-chain fragments,using a sequential set of 15 pre-assigned rules. Side-chain fragments are character-ized by having only one “break-point”, while scaffolds have two or more. The break-points are not always based on a logical retro-synthetic step, but are defined in a man-ner that allows us to analyze the composition of large volumes of compounds. Therules encoded and used in this study to generate these fragments are detailed inTable 6.1. After the molecules are decomposed, the resulting fragments (with the ad-dition of explicit hydrogens) are then used to perform substructure searches to deter-mine the frequency of occurrence of these specific fragments in each set of analyzedmolecules (Fig. 6.2). We feel that, as long as the same process is used to dissect andassemble compounds, internal consistency insures transferability of statistics.

Five different data sets were used to perform the fragment analysis: marketedoral drugs (1192 compounds), marketed injectable drugs (308 compounds), com-pounds in clinical trials as listed in MDDR [16] (1943 compounds; marketed drugswere substracted from this set), and compounds from MDDR described as in “bio-logical testing”. (137 550). In addition, we subdivided the marketed oral drugs forwhich the approval date was available into a set of 332 “new oral drugs” approvedafter 1982, and a set of 859 “old oral drugs” approved before 1982. The final list of139 126 unique molecules was then subjected to the MS process. As a result of thefragmentation, 52 131 side-chains and 15 652 scaffold fragments were identified.19 914 fragments with four or more atoms (14 330 side-chains and 5584 scaffolds)were present in two or more structures and were therefore considered meaningfuland used in the subsequent analysis. For each group, we computed the mean fre-quency of all fragments present and used it to assign importance for each frag-ment. Table 6.2 summarizes the fragment statistics for each group.

In order to compare the fragment distribution between two groups, we used allfragments present with frequencies greater than the standard deviation from themean for each group. Thus, for comparison of frequencies of fragments in oraland injectable drugs, we took 54 fragments from oral drugs and 91 fragments frominjectable drugs, for a total of 122 unique fragments (145 total with 23 in common).For the comparisons of new vs old drugs, oral vs clinical, and oral vs MDDR biolo-gical testing, we ended up with 52, 174, and 335 unique and significant fragments,respectively. In order to compare the frequencies between groups, we used the fol-lowing difference function, shown here for the oral vs injectable comparison:

Diffi � 100 �abs freqoral

i � freqinjectablei

� �

max freqorali � freqinjectable

i

� � �1�

1156.3 Methodology Used in This Analysis

116 6 Structural Fragments in Marketed Oral Drugs

Table 6.1 Sequentially applied SMARTS queries used in Molecular Slicer algo-rithm. The bond breakages occur between the indicated pair of query atoms.The SMARTS contain locally developed extensions to the SMARTS language,most notably the relational operator. Isotopic labels are applied to designatepreviously perceived bond breakages, so the queries require non-isotopicatoms for new breakages. Adapted from Vieth at al. [1].

Sequence SMARTS Queryatomsbondbreak

Isotopiclabel forbreakatoms

Query name

1 a-[CH,CH2;R00*]-[N,O;R00*]-[CHR0,CH2R0]-a

1 2 +1 Heteroatom beta toa ring

2 [C0*D>1,c0*D>1]-[NH,ND2,ND3,OD2;R00*]-[CH,CH2;R00*]-a

1 2 +1 Heteroatom beta toa ring

3 [R;0*]-[CH2R0,NHR0,OR0;0*]-[R] 0 1 +2 Separate two ringsconnected by an atom

4 *-[CD3H,ND2;R00*](-a)-a 0 1 +3 Separate biphenyl

5 [a]-&!@[a] 0 1 +4 Break ring–ring non–ring bonds

6 [NR;0*]-[CD3R0;0*](=O)-[R] 0 1 +5 Separate biphenonefrom ring N

7 [NR;0*]-[CD2R0;0*]-[R] 0 1 +6 Ring nitrogen beta toa ring

8 [N0*,n0*;!H2]-[SR0;0*]-[CD>1,cD>1,ND>1]

0 1 +18 Break N-S bonds

9 [NR;0*]-[CD2R0;0*]-[CD2,CD3,OD2,ND2,ND3,aD2,aD3]

0 1 +9 Break Ring nitrogen– no ring carbon

10 a-[NHR0]-[CR0;0*](=O)-[OR0,NR0;0*]

0 1 +10 Separate anilines

11 [CRD>1;0*]-[NH,O;0*]-[CR0;0*](=O)-[NH,O;0*]

1 2 +11 Break N-C in ureas

12 [OD1H0]=[CD3R0;0*](-[ND2,ND3;0*]-[CD>1,aD>1;0*])-[CD>1,OD2,aD>1,ND>1;0*]

1 2 +11 Break amides

13 [OD1H0]=[CD3R0;0*](-[CD>1;0*])-[CH2;R00*]-[CH,CH2;R00*]-a

1 3 +12 Gamma carbonyls

14 [a;0*]-[CD3R0;0*](=O)-[D2,D3,D4;0*]-[D<4]-[D<4]

0 1 +6 Break phenone

15 [CR,NR]=[CR]-&!@[a] 1 2 +13 Another ring break-age connected byno-ring bond

If a fragment is found only in the oral drugs, the difference is 100%, however ifthe fragment is found in both groups with the same frequencies, the difference is0 %. For each of the significant fragments we computed the difference functionand binned the differences into ten bins. The plots of the difference functions areshown in Fig. 6.3a–d for oral vs injectable, new vs old, oral vs clinical and oral vsMDDR biological testing compounds. The specific comparisons were chosen tounderstand whether the property differences between these sets would translateinto differences in the nature and frequency of the fragments derived from eachset.

1176.3 Methodology Used in This Analysis

Fig. 6.2Comparison of most frequent scaffolds in oral (a) and inject-able (b) drugs. The numbers indicate the number of drugs con-taining the fragment. The means of physical properties(CLOGP, ON, rotbond) are significantly different for a and b.Reproduced from Vieth et al. [1].

6.4Analysis of Similarities of Different Drug Data SetsBased on the Fragment Frequencies

The distribution of frequency differences gives a breakdown of the number offragments (both side-chains and scaffolds) between groups (Fig. 6.3). For example,fragments having large differences in frequencies between the oral and injectabledrugs dominate the distribution, with median and mean differences of 90 % and74%, respectively. A similar distribution is observed for the differences betweenfragments in oral and clinical compounds. The median difference is slightly lowerat 85%, with the mean identical and indistinguishable from the oral vs injectablegroup at 75%. In contrast, the distribution of differences for the new vs old mar-keted drugs is more uniformly distributed, with no particular preference to anydifference bin. This is substantiated by the median value of 53% and the mean va-lue of 52%. Interestingly, the distribution of frequency differences of 335 signifi-cant fragments compared between marketed oral drugs and biological testingshows a largely uniform character, with a slight bias towards higher differencebins. The median of this distribution 68% and mean of 60 % suggests more oral-like than injectable-like fragment character.

Inspection of the side-chain and scaffold structures giving the largest frequencydifference between oral drugs and the three groups injectable, clinical, and biolo-gically active molecules reveals that many of these side-chains likely derive from

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Table 6.2 General statistics for fragments generated from different drug datasets.

Group Numberof mole-cules

Numberof fragmentswith matchin at leastonemolecule

Meanfragmentfrequency

STD Mean +STD

Number offragmentswithfrequenciesgreaterthan 1 STDaway fromthe mean

Marketed oraldrugs

1192 1630 0.27 0.70 0.97 54

Marketed injectabledrugs

308 762 1.19 1.67 2.86 91

Clinical compounds 1943 3065 0.28 0.71 0.99 157

MDDR biologicalcompounds

137 550 20 577 0.02 0.25 0.28 330

New oral drugs 332 779 0.59 0.98 1.57 39

Old oral drugs 859 1204 0.31 0.84 1.14 48

peptides, which are present in injectable, clinical, and biologically active moleculesbut are largely absent in oral drugs. As it is difficult to determine the intendedroute of administration in either the clinical or the biologically active sets, thesesets include compounds not intended for oral delivery and/or for which ADMEproperties are not optimized.

In order to shed light on the specific differences and similarities between frag-ments from different sets, we looked into the most and least different fragmentsfor each comparison groups. The oral vs injectable 100% difference bin contains15 significant scaffolds and two side-chains that occur in one group but not in theother, representatives of which are shown in Fig. 6.4a. As pointed out in our origi-nal work, the majority of scaffolds (14) present only in the injectable drugs areamino acids. Six fragments (three side-chains and three scaffolds) are found withalmost identical frequencies in oral and injectable drugs. These six are exempli-fied in Fig. 6.4b. Interestingly, ethanolamine scaffolds and selected tertiary aminesare present with very similar frequencies in oral and injectable drugs.

1196.4 Analysis of Similarities of Different Drug Data Sets Based on the Fragment Frequencies

Fig. 6.3Distribution of the fragment frequency differ-ences as a function of difference bins (Eq. 1).We added 100 difference bin (included in90–100 bin) to separately view the structureswhich occur only in one group. (a) The totalcount of 122 side-chains and scaffold frag-ments in oral vs injectable comparison.

(b) The total count of 52 side-chains and scaf-fold fragments in new (post-1982) vs old (pre-1982) marketed oral drug comparison. (c) Thetotal count of 174 side-chains and scaffolds fororal vs clinical comparison. (d) The total countof 335 side-chains and scaffolds for oral vsMDDR biological comparison.

When comparing the fragments with similar and dissimilar frequencies in newand old oral drugs, one finds some penicillin and sugar structures that are absentin the new drugs, with unnatural amino acid present with much greater frequencyin the new drugs (Fig. 6.5). These differences reflect to some degree evolution oftargets trends within the pharmaceutical industry. The pencillin-derived beta-lac-tam framework, common in many older antibiotics, has not been actively pursuedfor some time due to several factors, including developing drug resistance and theadvent of alternative anti-infectives such as the fluoroquinolones. By contrast,three of the five fragments appearing in newer drugs that are not reflected inolder drugs are related to homophenylalanine, a fragment common to many ACEinhibitors, such as enalapril, that have been launched in the past 20 years. As weand others have shown [1, 17], however, the physical property differences for newvs old oral drugs are generally very small. These fragment differences, notablysmaller than for the other comparisons, likely reflect target-related differences.The fragments common to both new and old drugs, such as 2-substituted pyri-dine, isopropyl and phosphate side-chains, and propylamine, butyl, and butyl-amine, are perhaps more generic in helping to confer favorable PK/PD propertiesand biological activity on oral drugs. These conclusions, although similar in spiritto the ones presented by Leeson [2], come from the analysis of building blocksand thus may have more direct application particularly in the area of drug-likelibrary design and construction.

120 6 Structural Fragments in Marketed Oral Drugs

Fig. 6.4Exemplification of significant fragments most different (a) andmost similar (b) between oral and injectable drugs. The countsand percentages of structures in which each fragment is pre-sent are given for both groups.

121

Fig. 6.5Exemplification of significant fragments most different (a) andmost similar (b) between new (post-1982) and old (pre-1982)drugs. The counts and percentages of structures in which eachfragment is present are given for both groups.

Fig. 6.6Exemplification of significant fragments most different (a) andmost similar (b) between oral and clinical drugs. The countsand percentages of structures in which each fragment is pre-sent are given for both groups.

As noted above, fragments with the greatest oral vs clinical differences are simi-lar to those from oral vs injectable differences (amino acid scaffolds; Fig. 6.6).Fragments with almost identical frequencies in marketed oral and clinical com-pounds include phenyl and 2-substituted pyridine side-chains, and para-substi-tuted benzyl, 2-alkoxy-substituted phenyl, and 1,3 di-substituted cyclohexyl scaf-folds.

The fragments with the largest differences between oral drugs and biologicallyactive compounds also include amino acid side-chains (similar to clinical and in-jectable compounds), while the notable fragments with similar frequencies in the

122 6 Structural Fragments in Marketed Oral Drugs

Fig. 6.7Exemplification of significant fragments most different (a) andmost similar (b) between oral and MDDR biological testingdrugs. The counts and percentages of structures in which eachfragment is present are given for both groups.

two groups include piperidine and pyridine scaffolds, in addition to several al-ready exemplified in earlier comparisons (Fig. 6.7).

6.5Conclusions

As we pointed out in our earlier communication, there is some association ofproperties of drug types and their derived fragments, in particular when compar-ing oral and injectable drugs. The apparent difference in some properties (in-creased MW by 10 %) in new vs old drugs observed by Lesson does not appear tobe related to the properties of the most commonly occurring fragments in thesegroups. The 39 significant fragments in new drugs have a mean MW of 90 Da,while the 48 significant fragments in old drugs have a slightly higher mean MWof 93 Da. As Vieth and Leeson pointed out, there is no significant difference inyear to year mean MW of approved drugs, which seems to be consistent withsmall changes in frequently used fragments in the whole groups of old and newmarketed oral drugs. It may be that the most significant differences in fragmentsbetween older and newer drugs reflect a shifting biological target emphasis overthe past 25 years [1, 2]. Our analysis suggests that building compounds from mar-keted oral drug fragments should increase the bioavailability of compounds, com-pared to random selection. It is intriguing that there are many fragments com-mon to nearly all groups and routes of administration, suggesting they may begenerally useful for obtaining biological activity while also proving beneficial for,or at least not detrimental to, reasonable oral exposure. By contrast, many of thefragments that appear frequently in injectable, clinical, and biologically activecompounds but not oral drugs are reflective of peptide substructures. These frag-ments clearly have biological relevance as well. Peptides themselves frequentlydisplay poor PK/PD properties; this however may be more a reflection of the prop-erties of the molecules as a whole rather than the individual fragments. At thesame time, many of the particular amino acid-related side-chains that appear fre-quently in injectable but not oral drugs derive from amino acids such as trypto-phan and histidine that may suffer from metabolic liabilities on oral dosing.

We believe that, as researchers continue to analyze fragment differences be-tween classes of drugs and between drugs and biologically active molecules ornon-drugs, these differences will help to shed light on property differences be-tween the molecules as a whole. In addition, as we demonstrated here, the signifi-cant fragments could tell the story about the resulting molecules constructedfrom them.

1236.5 Conclusions

Acknowledgments

The authors would like to thank Dan Robertson, Ken Savin, Phil Hipskind, andIan Watson for their contribution to Molecular Slicer. Dr. Tudor Oprea is acknowl-edged for helpful comments on the chirality of fragments and molecules in thedatabase.

124 6 Structural Fragments in Marketed Oral Drugs

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