Jonathan S. Mason,, Isabelle Morize, Paul R. Menard,*, Daniel L. Cheney, Christopher Hulme, and Richard F. Labaudiniere

Transcription

1 J. Med. Chem. 1999, 42, New 4-Point Pharmacophore Method for Molecular Similarity and Diversity Applications: Overview of the Method and Applications, Including a Novel Approach to the Design of Combinatorial Libraries Containing Privileged Substructures Jonathan S. Mason,, Isabelle Morize, Paul R. Menard,*, Daniel L. Cheney, Christopher Hulme, and Richard F. Labaudiniere Computer-Assisted Drug Design and Lead Discovery, Rhône-Poulenc Rorer, 500 Arcola Road, Collegeville, Pennsylvania Received December 11, 1998 A new 4-point pharmacophore method for molecular similarity and diversity that rapidly calculates all potential pharmacophores/pharmacophoric shapes for a molecule or a protein site is described. The method, an extension to the ChemDiverse/Chem-X software (Oxford Molecular, Oxford, England), has also been customized to enable a new internally referenced measure of pharmacophore diversity. The privileged substructure concept for the design of high-affinity ligands is presented, and an example of this new method is described for the design of combinatorial libraries for 7-transmembrane G-protein-coupled receptor targets, where privileged substructures are used as special features to internally reference the pharmacophoric shapes. Up to 7 features and 15 distance ranges are considered, giving up to 350 million potential 4-point 3D pharmacophores/molecule. The resultant pharmacophore key ( fingerprint ) serves as a powerful measure for diversity or similarity, calculable for both a ligand and a protein site, and provides a consistent frame of reference for comparing molecules, sets of molecules, and protein sites. Explicit on-the-fly conformational sampling is performed for a molecule to enable the calculation of all geometries accessible for all combinations of four features (i.e., 4-point pharmacophores) at any desired sampling resolution. For a protein site, complementary site points to groups displayed in the site are generated and all combinations of four site points are considered. In this paper we report (i) the details of our customized implementation of the method and its modification to systematically measure 4-point pharmacophores relative to a special substructure of interest present in the molecules under study; (ii) comparisons of 3- and 4-point pharmacophore methods, highlighting the much increased resolution of the 4-point method; (iii) applications of the 4-point potential pharmacophore descriptors as a new measure of molecular similarity and diversity and for the design of focused/biased combinatorial libraries. Introduction Pharmacophore Methods. Methods for molecular similarity and diversity that are relevant to drugreceptor interactions are needed for many computerassisted drug design (CADD) applications. In fact, the increased use of combinatorial chemistry and highthroughput screening in pharmaceutical drug discovery research requires methods for design and analysis that are able to handle rapidly large numbers of structures, often of a relatively high conformational flexibility. These design and analysis requirements led us to expand upon our previous work using 3-point pharmacophores 2,3 to the use of 4-point pharmacophores and also to the development of methods that internally reference the similarity/diversity measure relative to a special feature/substructural motif of interest. While the 3-point pharmacophore method was very useful, 4-6 we considered it extremely important to move Computer-Assisted Drug Design. Lead Discovery. Current address: Computer-Assisted Drug Design, Bristol-Myers Squibb, P.O. Box 4000, Princeton, NJ To whom correspondence concerning the 4-point pharmacophore method should be addressed: , masonj@bms.com. to 4-point pharmacophores to get an increase in the amount of shape information and resolution, including the ability to distinguish chirality, a fundamental requirement for many ligand-receptor interactions. The increased resolution and the results of the ligandprotein similarity studies reported here confirm that this is indeed a major improvement, with an associated technical advance in being able to rapidly fingerprint a molecule, resolving up to 350 million 3D pharmacophoric shapes. The ability to do this including extensive conformational flexibility was also considered essential, especially as many of the ligands of interest and combinatorial library compounds being produced are fairly flexible. This flexibility makes inadequate the use of a single (or a small limited set of) conformer(s) to represent the accessible conformational space and thus the potential bound receptor conformation of a ligand. Earlier experiences with the development of a method that uses individual 3D queries 2 led us to collaborate with Chemical Design to expand the ChemDiverse module of Chem-X 1 that uses 3-point pharmacophores, to 4-point pharmacophores, retaining a relatively fast analysis (<1 min/molecule including conformational /jm CCC: $ American Chemical Society Published on Web 08/07/1999

2 3252 Journal of Medicinal Chemistry, 1999, Vol. 42, No. 17 Mason et al. sampling). Key features of the software that makes possible the development of these approaches are its ability to rapidly generate conformations at search time and systematically identify all potential pharmacophores for each conformer. Privileged Substructures. Certain select substructures, so-called privileged substructures, 7 are able to provide high-affinity ligands for more than one type of receptor or enzyme. This concept has been used in the past by medicinal chemists to design inhibitors of some families of enzymes around functional groups or substructures known to interact directly with some specific binding sites within the active site. Examples, among others, are hydroxamate- and benzamidine-based derivatives for zinc metalloproteinases and serine proteases, respectively. Variations on the substitution pattern on the backbone bearing the privileged substructure or on the backbone itself confer specificity elements toward one member or a subset of the distinct family of enzymes considered. Many examples illustrating the hypothesis of privileged substructures proposed by Evans et al. in for the broad class of 7-transmembrane G-proteincoupled receptors (7TM-GPCRs) have been published in the last 10 years. Besides the classical diphenylmethane group, other 7TM-GPCR privileged substructures can be proposed which are capable of providing many different useful ligands, often for more than one GPCR receptor as an antagonist and even as agonists. These include biphenyltetrazole (BPT), spiropiperidine, indole (tryptophan), and benzylpiperidine; several examples (nonexhaustive list) are illustrated in Figure 1. Benzodiazepine or benzazepine is obviously another example of a GPCR privileged substructure either by itself or as an example of a constrained diphenylmethane moiety (see Figure 1). Again in this case, specificity results from varying the substitution pattern or the backbone attached to the privileged substructure. The combination of this concept of privileged substructure with the power of combinatorial chemistry to prepare large sets of diverse compounds around a common chemical motif should allow a faster identification of new agonists, antagonists, or inhibitors for new members of a known receptor or enzyme biological family. 8 Combined with the appropriate genomic effort to identify and select new members of known biological target families, this approach should ultimately foster the drug discovery process. Privileged Substructures and the Pharmacophore Method. Thus, a novel modification to the 4-point pharmacophore method was made to force one of the points to be a special feature associated only with the privileged substructure. This feature, normally a dummy atom defined as the centroid of the substructure, is thus part of the pharmacophore definition, i.e., 1 of the 4 points must be this special feature. This enables us to focus the similarity/diversity measure around these privileged substructures, to produce an internally referenced measure of similarity/diversity that is orientation-independent and can be used to compare different molecules and sets of molecules; i.e., the similarity or diversity measure is now relative to the special feature: differences in size or pharmacophoric features relate to differences in pharmacophoric shapes that center around the special feature. Similarly, this special feature can be assigned to any motif or group of interest, providing new profiling and design methods: for example, to evaluate reagents based on their pharmacophore diversity relative to the attachment point or on their similarity to complementary pharmacophores for a protein site, both being calculated relative to the attachment point (e.g., of a docked scaffold for the protein site). With such a modification to the original 4-point pharmacophore method, we were able to design libraries that optimize both the coverage of pharmacophoric shapes found in known active ligands and the exploration of new diversity with the option of focusing this measure around a privileged substructure of interest. This internally referenced molecular similarity/diversity measure has been applied to the design of combinatorial libraries for several targets, such as the 7TM-GPCR ones discussed below. The methods described in this paper are extensive customizations and modified applications of software now available as the 4-center pharmacophore extension to the ChemDiverse module of Chem-X. Computational Methods Generation of the Potential Pharmacophores for Ligands. Features that are likely to be important for drug-receptor interactions are automatically identified for each molecule through the assignment of atom types and the addition of dummy atoms. The six key features used in this work are hydrogen bond donors; hydrogen bond acceptors; acidic centers (negatively charged at physiological ph 7); basic centers (positively charged at ph 7); hydrophobic regions; aromatic ring centroids. Quaternary nitrogen centers are also identified and can be optionally grouped with the positively charged basic centers or assigned to the extra (7th) feature available. Features are referred to as centers in Chem-X, and Table 1 shows the associated center numbers, symbols, and definitions. The ability to carefully distinguish atomic environments and to equivalence those with similar properties has been found to be key for molecular similarity/ diversity evaluations. For example, a carboxylic acid, a tetrazole, and an acyl sulfonamide are all acids ionizable at physiological ph and by default should be equivalenced as acids. On the other hand, deactivated atoms such as monosubstituted nitrogens in aromatic systems and disubstituted nitrogens in amides need to be excluded from the list of potential interacting atoms/ groups. Tautomeric atoms (e.g., imidazole N) should be assigned as donor or acceptor. The definition of specific atom types, rather than the use of generic element types, offers a powerful method to achieve this, and a customized version of the Chem-X atom types was used for this work. Details of relevant atom type customizations and their association to pharmacophore features are given in Table 2. In a first step, atom types are assigned automatically when reading a molecule into Chem-X through a fully user-customizable parametrization file and fragment database; this process is discussed further in the Experimental Section. Second, each useful atom type for the pharmacophore feature definition is assigned one

3 4-Point Method for Molecular Similarity/Diversity Journal of Medicinal Chemistry, 1999, Vol. 42, No Figure 1. Examples of 7TM-GPCR privileged substructures. Table 1. Pharmacophore Features ( Centers ) Used to Define the 3- and 4-Point Potential Pharmacophores center number center D A P R L C B symbol definition H-bond donor H-bond acceptor reserved privileged aromatic hydrophobic region acidic atom basic nitrogen or A +/or D ring centroid

4 3254 Journal of Medicinal Chemistry, 1999, Vol. 42, No. 17 Mason et al. Table 2. Atom Type Customizations and Pharmacophore Feature Assignments ( Center Number) a center atom type no. SMILES Nitrogen 14A 2 CCtN 14B 2 CCdNC 14C 7 C[N + ](C)(C)C 14D 7 CCN 14E 1 C(dO)N 14F 1 c1ccc[nh]1 14G 2 c1ccccc1ndno 14H 2 n1ccccc1 14M 1,2 [3] CCdN 14N 1 CNNC 14P 2 CN(C)N(C)C 14Q C(dO)N(C)C 14R 2 CS(dO)(dO)N(C)C 14S 1,2 [3] CS(dO)(dO)N 14T 6 Cc1n[nH]nn1 14U 7 CC(dN)N 14U1 1 c1cccnc1n 14V 1 c1ccccc1n 14W 7 CN(C)C 14X c1ccccc1n(c)c 14Y 1,2 [3] c1[nh]cnc1 00Q 3 special or privileged feature Oxygen 16A 1,2 [3] CO 16B 2 C(dO)C 16C 2 o1cccc1 16D 6 C(dO)O 16E 2 COC 16F 1,2 [3] c1ccnc(o)c1 16G [N + ]([O - ])(do) 16H 6 CS(dO)(dO)O Sulfur 32A 2 CSC 32B CS(dO)C 32C CS(dO)(dO)C 32D 2 C(dS)N Aromatic Ring Centroid 00C 4 aromatic 6-ring 00D 4 aromatic 5-ring Lipophilic 00E, 00L, 00M, 5 lipophilic regions 00N, 00O, 00P a Only the feature number is used in the potential pharmacophore analysis; the more detailed separation by atom types is useful for associating features according to different environments for an atom (in a way that can be readily modified) and for 3D database searching. 2 The number 3 in brackets refers to its assignment by us in 7 full-feature mode (no special feature being able to be used) to features that can be both donors and acceptors; normally a 6 full-feature mode was used, with the remaining feature (number 3) only being used for the special ( privileged ) feature. or more of the seven features (Chem-X centers ). Dummy atoms, with associated atom types and hence feature assignments, are added to represent the hydrophobic regions (by an automatic method within Chem-X that uses bond polarities 2 ) and to represent aromatic ring centroids (geometric ring centroids). The distances between pharmacophoric features for all the evaluated conformers are calculated exactly but stored using a binning scheme, with each distance represented by the bin into whose range it falls; see Table 4 and the Experimental Section for further details. All valid combinations of features and distance ranges define the potential pharmacophores, the presence of which in any conformer of a molecule sets a bit Figure And 4-point pharmacophores. Figure And 4-point potential pharmacophores for an endothelin antagonist compound (combinations from 6 features/ point). in a pharmacophore key ; see Figures 2 and 3 for the definition and an example of 3- and 4-point pharmacophores. An optional added constraint to the 4-point pharmacophore calculation is to include chirality. This means that for all chiral combinations of features separate bits in the pharmacophore key are set for the two enantiomers ; distances alone cannot distinguish chirality. This is particularly important when chiral information is available, for example, when using complementary pharmacophores from a protein active site, and was considered a requisite for an effective similarity/diversity measure given the large difference in biological activity that can be observed between drug enantiomers. This option became available during these studies and was used for the ligand-protein similarity studies. In these studies the use of chiral potential pharmacophores was found to increase the resolution of selectivity between the different serine proteases. Conformer Generation. The relatively high conformational flexibility of many structures requires that an effective conformational sampling is performed for a pharmacophore-based analysis. The method used for this work using ChemDiverse is based on an on-thefly generation of conformers done at search time. A quick evaluation of the conformation was performed based on a steric contact check to reject poor or invalid conformations; the default ChemDiverse method uses rules. Both methods are much faster than a full energy calculation and avoid using only the lowest energy conformers whose relevance for binding is not clear. While this sampling method is still limited, the example figures given in the Experimental Section (an average of about 2200 conformations accepted, from sampled) show that far more than a small predefined stored set (e.g., of 100 conformations) is used. Generation of the Potential Pharmacophores for Protein Sites. Potential 3- or 4-point pharmacophores for an enzyme active site or a receptor site can be calculated in a similar manner using complementary

5 4-Point Method for Molecular Similarity/Diversity Journal of Medicinal Chemistry, 1999, Vol. 42, No site points. Site points can be generated by several different methods; the method in Chem-X/ChemProtein (Receptor Screening) uses a customizable template database where complementary site points for all residue types are defined and positioned relative to the residue atoms. For example, a hydrogen bond donor atom would be placed complementary to an accessible backbone carbonyl oxygen atom, while a hydrogen bond acceptor would be placed to face an amide protein backbone nitrogen. Where relevant, site points that have both donor and acceptor capabilities (i.e., assigned to both features) are placed in appropriate positions, such as those for a serine or threonine hydroxyl group. Several theoretical, but generally experimentally trained, methods such as GRID 9 and SITEPOINT 10 can be used to identify and locate the complementary site points. In our case the GRID method was extensively used, which performs energetic surveys of the site, using a wide variety of probe atoms or groups. From the resultant maps, energetically contoured, it is thus possible to locate complementary site points for hydrogen bond acceptor, hydrogen bond donor, hydrogen bond acceptor and donor, acidic, basic, hydrophobic, or aromatic interactions. An automatic location of dummy atoms at energetic minima is also available as a GRID program option. These positions of potential interacting atoms or groups can be verified and optimized using a force-field molecular dynamics simulation and/or minimization. The SITEPOINT method is based on crystallographically determined positions of high or maximum probability for hydrogen bond acceptor, hydrogen bond donor, acidic, and basic groups only. The effect of protein conformation on the position of site points may depend on the type of protein under consideration. For example, overlays of crystal structures available from the Brookhaven Protein Data Bank of the trypsin-like serine proteases factor Xa, thrombin, and trypsin reveal very small variabilities, which we assume are negligible in the context of the binning schemes used in pharmacophore generation; i.e., the relatively large distance range for each feature-feature distance (e.g., (15% tolerance from midpoint) should allow for the protein site flexibility (and resultant site point movement) in this case. When conformational variability is observed however, it may be necessary to choose several structures that best represent the range of side chain variability. Site points and pharmacophore keys can then be calculated for each structure and Boolean combinations used to produce common or union keys. Once generated, as for a ligand, all combinations of four site points are considered and stored in a pharmacophore key. They can also be used directly to generate individual queries for 3D database searching. The Ligand-Receptor Site Comparison results discuss a new method for 3D ligand-protein similarity and 3D database searching that uses these multiple potential pharmacophores. Inclusion of a Privileged Feature into the Pharmacophore Definition. A powerful extension to the pharmacophore method was developed whereby one of the points is forced to contain a special pharmacophore feature defined specifically to code for a privileged substructure, as illustrated in Figure 4. All the potential pharmacophores kept in the pharmacophore Figure 4. Example of a special pharmacophore feature to code the biphenyltetrazole privileged substructure. key then contain this feature and make it possible to reference the pharmacophoric shapes of the molecule relative to the privileged substructure. This gives an internally referenced or relative measure of the molecular similarity/diversity. The measure is relative to the special feature that is forced to be in each pharmacophore, which is assigned to an atom or substructure (as a centroid) of interest; the other pharmacophoric features all then have a distance to the special point in their definition, and thus distances and resultant tetrahedral shapes can be evaluated and compared relative to this point. Either an atom type unique to the group of interest (e.g., for a carboxylic acid) or a dummy atom centroid of a substructure of interest was assigned the special feature. While in this paper we discuss the use of this method where the special feature is a known privileged group for 7TM-GPCR targets, the method can clearly be used for many other applications. Examples are to explore a given subsite in a protein active site or to explore the diversity at a specific position for combinatorial library compounds. In this case the privileged substructure will be the scaffold itself and the special feature will be located at the attachment point of interest. To explore a particular subsite in a protein active site, in addition to the special coding of the molecules being docked, one will also have to position this special feature as a site point where the molecule to dock will be anchored. This can be done using the position of the atom of the docked scaffold which is used to link the reagents/substituents. All the potential pharmacophores that include the complementary site points of the subsite of interest and the special site point are thus calculated and stored. The next step is to compare the pharmacophores calculated for the protein subsite with the ones calculated for the molecules to dock in the site. An extension of the method that enables the steric shape of a protein site to be used as an additional constraint in the comparison of multiple potential pharmacophores of a protein site with a ligand has been developed (DiR module 11,12 ). This is equivalent to simultaneous 3D database searching using multiple 3D pharmacophoric queries and steric constraints, but with only one conformational sampling being necessary. Results and Discussion Reference Databases. To evaluate the power of the 3- and 4-point pharmacophore methods, several analyses and comparisons were performed using various sets of compounds. These include compounds from the MDL Drug Data Report (MDDR), 13 the Available Chemical Directory (ACD), 13 and the RPR corporate registry

6 3256 Journal of Medicinal Chemistry, 1999, Vol. 42, No. 17 Mason et al. Figure 6. 2D (Daylight fingerprints, Tanimoto coefficient) and 3D (underlined number) (4-point multiple potential pharmacophores, modified Tanimoto-style coefficient with a combination of dynamic and fixed weightings that reduce the penalties for the additional unmatched potential pharmacophores exhibited by each compound) similarity between some angiotensin II antagonists. Figure 5. 4-Point potential pharmacophore results from reference databases (6 features/point, 10 distance ranges) using all structures (A) or random sets of 14K structures each (B). database. Figure 5 illustrates the total number of 4-point potential pharmacophores exhibited in each of the databases, summed for all compounds; previous studies 14 showed the increase in resolution possible using 4-point instead of 3-point potential pharmacophores. Molecular Similarity Validation Studies Using 4-Point Pharmacophores. 1. Ligand-Ligand Comparison. To investigate the use of the 4-point pharmacophore method in molecular similarity analyses, molecules with similar biological activities were compared. These comparisons were done for a set of compounds exhibiting 2D similarity (as measured with the Tanimoto 15 coefficient using Daylight fingerprints 16 ) and for a set of compounds with no significant 2D similarity but for which similar biological activities were reported. An example of ligands with reasonably high 2D fingerprint similarities is angiotensin II (AII) antagonists, where the design of a new series was usually based on existing 2D structural information. In such cases 2D fingerprints will tend to group actives together. For example, an analysis of the MSD, Takeda, and ICI compounds (from MDDR database) shown in Figure 6 shows they share reasonable 2D similarities (Tanimoto Figure 7. 2D (Daylight fingerprints, Tanimoto coefficient) and 3D (underlined number) (4-point multiple potential pharmacophores, modified Tanimoto-style coefficient with a combination of dynamic and fixed weightings that reduce the penalties for the additional unmatched potential pharmacophores exhibited by each compound) similarity between some endothelin A antagonists. coefficient using Daylight fingerprints between 0.54 and 0.87). However a similar analysis of endothelin A antagonists (ETA) shows no significant 2D similarities between active compounds 17 (see Figure 7) (Tanimoto Daylight fingerprint similarities at or below significance, around 0.4 or below). With this target many new series were identified by screening, often using directed sets from 3D database searching, leading to compounds that are much more 2D structurally diverse. In both cases, there is a significant overlap of the potential 4-point pharmacophores of the ligands: 1000 for the AII compounds, for the ETA compounds using 7 distance ranges and 6 features. To quantitatively evaluate similarity between compounds, a modified Tanimoto-style coefficient with special weightings was developed for the 4-point multiple 3D potential pharmacophore method. Weighting was found to be necessary to avoid overpenalizing the additional unmatched pharmacophores, the total number of potential pharmacophores identified varying greatly from molecule to molecule (for example, from the addition of a new functional group and/or greater flexibility). A similar approach was used for the calcula-

7 4-Point Method for Molecular Similarity/Diversity Journal of Medicinal Chemistry, 1999, Vol. 42, No Figure 8. 4-Point potential pharmacophores for angiotensin II antagonists, the equation used to calculate the dynamically weighted Tanimoto-style coefficient similarity values and the resultant values with dynamic and nondynamic (W_dynamic ) 1) weighting. Ncommon ) pharmacophores in common between molecules mol and refmol ; Nmol_only ) pharmacophores exhibited by molecule mol being analyzed not in common with the reference molecule; Nrefmol_only ) pharmacophores exhibited by reference molecule not in common with mol. Either of a pair of molecules could be considered to be refmol and the highest similarity retained; when searching a database using pharmacophoric information from an active molecule(s), that data is normally Nrefmol. tion of molecular surface similarities. 18 An additional dynamic weighting factor was also used, to further reduce the penalty for molecules having these additional pharmacophores not in common with the reference compound the nearer the number of common pharmacophores is to the number of potential pharmacophores in the reference compound (see Figure 8). This was found to improve the signal-to-noise ratio (which can be evaluated as the similarity of biologically similar compounds versus random compounds). These modified coefficients will thus alter the relative ranking and ordering of compounds, with the aim of increasing the ranking of compounds that have a high number of common pharmacophores with the reference compound but also have large numbers of additional potential pharmacophores. The absolute weighting values were also chosen to give coefficients for biologically similar compounds that had a similar range of significance to Tanimoto coefficients from 2D fingerprints (e.g., >0.8 very similar, > of some significance). For both the angiotensin and endothelin cases a reasonable to good similarity is found using this dynamically weighted Tanimoto-style coefficient with 4-point pharmacophores (see underlined numbers in Figures 6 and 7). Details of the formula used and some of the exact numbers of total and common potential pharmacophores are shown in Figure 8. Using 3-point pharmacophores a reasonable level of similarity can also be found but with a much higher noise level (similarity values for compounds not expected to have similar activity). The 4-point model appears to increase the number of common potential pharmacophores more for compounds with similar activity and thus reduces the noise level. For example, with the endothelin compounds using 4-point pharmacophores, many other structures in the MDDR had very low similarity; the higher values found were generally for compounds reported to be 7TM-GPCR ligands (such compounds may indeed have some endothelin activity if they were tested, and the similarity could represent some general properties of 7TM-GPCR ligands). The dynamic weighting was found to improve the differentiation of expected actives and nonactives, by increasing the score for compounds that match a high proportion of the reference compound potential pharmacophores by reducing the penalty for additional (and thus not in common) pharmacophores; such compounds thus become ranked higher than some compounds with a lower number of both common and additional potential pharmacophores. The endothelin results also show the importance of correctly identifying features through the parametrization database. In fact, the similarity of the RPR and MDDR compounds is dependent upon the acyl sulfonamide of the MDDR compound being identified as an acid (a group that is very different in 2D similarity to a carboxylic acid); removing that assignment reduces the 3D pharmacophoric similarity significantly (by 0.15). 2. Ligand-Receptor Site Comparison. The 4-point pharmacophore method can also be used as a means to measure similarity when comparing ligands to their binding site targets. The pharmacophore key calculated from a ligand can be compared to the pharmacophore key of its target binding site, and as previously done between ligands, a similarity index can be calculated. The pharmacophore key for the site is generated from complementary site points, as discussed earlier. An example of this come from studies on three closely related serine proteases: trypsin, thrombin, and factor Xa. Site points were manually located on the basis of GRID-generated energy contour maps using the chemical probes sp 2 CH as hydrophobe, amide NH as hydrogen bond donor, carbonyl oxygen as hydrogen bond acceptor, anionic carboxylate group as acid, and cationic amidine

8 3258 Journal of Medicinal Chemistry, 1999, Vol. 42, No. 17 Mason et al. Figure 9. Number of potential 3- and 4-point pharmacophores calculated on the basis of complementary site points inserted into the active sites of thrombin, factor Xa, and trypsin. group as base (see Figure 9). 3- And 4-point pharmacophore keys were generated, and corresponding keys were also generated using full conformational flexibility for two series of highly selective and potent factor Xa and thrombin inhibitors (see Table 3 and Figure 10). We were interested in whether receptor-based similarity as a function of common potential pharmacophores for each ligand/receptor pair could resolve enzyme selectivity. Using 3-point pharmacophore-based similarity, poor resolution of enzyme selectivity was observed (see Figure 10). For example, the thrombin inhibitors NA- PAP, MQPA, and BM do not have a uniformly greater number of pharmacophores in common with the pharmacophore key generated from the thrombin active site compared to the keys generated from factor Xa or trypsin. When a similar analysis is conducted using 4-point pharmacophores, enhanced resolution of enzyme selectivity is observed. Factor Xa and thrombin inhibitors exhibit greater similarity with the complementary pharmacophore keys of the factor Xa and thrombin active sites, respectively, than with the pharmacophore keys generated from the other enzymes. To evaluate this metric in a broader context of ligand-receptor similarity, the above analysis was repeated using two fibrinogen receptor antagonists taken from the MDDR (MDDR and MDDR ). These compounds resembled trypsin-like serine protease inhibitors in terms of 2D structural features (benzamidine) but had no reported activity for this class of enzymes. Using 3-point pharmacophore profiling both molecules exhibited significant Table 3. Enzyme Selectivity for a Set of Selective Thrombin and Factor Xa Inhibitors Resolved in Terms of Relative Numbers of Common Potential 4-Point Pharmacophores for Each Ligand/Receptor Pair a number of common pharmacophores molecule K i point factor Xa thrombin trypsin MQPA 19 nm vs 3-pt b 128 thrombin 4-pt b 82 NAPAP 6 nm vs 3-pt b 99 thrombin 4-pt b 82 BM nm vs 3-pt b 31 thrombin 4-pt b 40 RPR nm vs 3-pt 56 b factor Xa 4-pt 59 b DX nm vs 3-pt 32 b factor Xa 4-pt 10 b 4 1 MDDR pt pt MDDR pt pt a Selectivity is not resolved using 3-point multiple potential pharmacophores in a similar analysis. Inactive ligands with similar 2D structures have low similarity only at the level of 4-point multiple potential pharmacophores. b Number of pharmacophores in common with the enzyme for which the inhibitor is selective. similarity against all three enzymes, while with 4-point pharmacophores the degree of similarity diminishes very substantially. Identification of Enriched/Specific Pharmacophores for an Ensemble of 7TM-GPCR Targets. As an extension to the ligand-ligand comparisons, pharma-

9 4-Point Method for Molecular Similarity/Diversity Journal of Medicinal Chemistry, 1999, Vol. 42, No Figure 10. Structures of selective thrombin and factor Xa inhibitors used for the enzyme selectivity studies in Table 3. cophoric shapes of sets of molecules sharing similar activity were searched with the goal of identifying an ensemble of specific pharmacophores (or an ensemble enriched in particular pharmacophores) to bias the design of libraries for 7TM-GPCR targets. In particular analyses were carried out using a set of 7TM-GPCR ligands, a set of molecules known to inhibit enzymes, and a set of random compounds from the ACD with similar flexibility. 14 By analyzing 4 random subsets of 200 compounds, it was possible to identify pharmacophores that were unique to, or enriched for, the 7TM- GPCR ligands; the 4-point pharmacophore approach found a much higher percentage (42%) unique than the 3-point approach (13%). 14 The advantage of the 4-point method in resolving a higher proportion of characteristic information was also shown not to be just due to the number of pharmacophores involved but also to their content; the use of 32 distance ranges led to a similar number of 3-point pharmacophores as the 4-point method with 7 ranges, but only 24% unique to the 7TM-GPCR set. A 4 times occurrence was effectively used in these studies to define a common pharmacophore, but an actual count can be used; using 3-point pharmacophores 15% of the pharmacophores occur at least 4 times more frequently in the 7TM-GPCR set than in the inhibitor set, decreasing to 2% with a count ratio threshold of 10. Pharmacophores Containing a Privileged Feature and Design of Combinatorial Libraries Enriched in Pharmacophores Found in 7TM-GPCR Ligands. We discussed above the inclusion of a special feature in the pharmacophore definition to measure the molecular pharmacophoric similarity/diversity relative to a given feature, for instance, a privileged substructure. The frequent occurrence of certain substructures in 7TM-GPCR ligands that are believed to be important for biological activity was also discussed. Figure 11 shows the results of searches in the MDDR for four such substructures: diphenylmethane, biphenyltetrazole, spiropiperidine, and indole. To complement our study and provide a practical basis for the design of new combinatorial libraries, we parametrized several of these privileged motifs as the special feature. We then calculated only the 4-point pharmacophores that include the special feature for sets of compounds bearing the Figure 11. Overview of the 7TM-GPCR privileged motifs found in MDDR (version 96.1). A ) any atom type, not in a ring; A-A bond ) single, double, or aromatic. same privileged motif and known to act on 7TM-GPCRs. This generated a pharmacophore key where similarity/ diversity is measured in a relative sense to the privileged substructure and which can be used to help focus libraries on products with 3D properties similar to those of the known active compounds. In practice, to produce libraries for 7TM-GPCR screens, the 7TM-GPCR privileged substructures can be incorporated into the reagents used for a condensation reaction (e.g., the amine, acid, aldehyde, or isonitrile used in the Ugi reaction ), as a scaffold, and/or be formed as part of the reaction (e.g., benzodiazapine 25,26 ). The design goal can be to mimic the 3D pharmacophores of existing active structures and/or to explore new pharmacophores to fill missing diversity of pharmacophoric shapes around the privileged substructures. This could be used, for example, to produce libraries for new 7TM-GPCR screens that do not have existing small molecule ligands. It is also possible to simultaneously optimize the total pharmacophoric diversity of the library. Several examples have already been published around the idea of mimicking known active molecules by using substructures present in these molecules as reagents in combinatorial synthesis. 8 In our case, privileged motifs were also introduced as reagents to produce combinatorial libraries, but we biased the design one step further by enriching the libraries with pharmacophores found in known 7TM- GPCR ligands. To illustrate this approach, biphenyltetrazole (BPT) was chosen as the privileged motif using chemistry based on the Ugi reaction (see Figure 12). The BPT fragment was added to the parametrization database, using a centroid dummy atom to code this privileged substructure as shown in Figure 4 for one of the MDDR compounds. New Chem-X procedures were written to select reagents on the basis of their contribution (at the product level) to the coverage of the 7TM-GPCR pharmacophore space and to the exploration of new diversity around the privileged substructure. In other words,

10 3260 Journal of Medicinal Chemistry, 1999, Vol. 42, No. 17 Mason et al. Figure 12. Example of Ugi chemistry with BPT incorporated as privileged group at the amine position. reagents were selected when their corresponding products maximized the number of pharmacophores in common with the ones calculated for the 7TM-GPCR MDDR subset of compounds exhibiting BPT in their structure. To avoid always covering the same pharmacophores, the selection procedure has to be dynamic: i.e., the pharmacophores from the 7TM-GPCR pharmacophore key that are covered by the products containing the selected reagents have to be removed for the next step of the selection. While optionally reagents can also be selected if they just add new pharmacophores, not especially the ones found in 7TM-GPCR ligands, in practice this was found to be more or less correlated with the selection based on the 7TM-GPCR overlap. When the number of reagents is very large, then a preselection is done to create a subset that is pharmacophorically and/or 2D structurally diverse. In such cases, the pharmacophore calculations are done with the reagent transformed to be as product-like as possible (i.e., represent reagent as it will be in the product, for example, include the core structure and some fixed choices for the other reagents). A schematic outline of the library design procedure is described in Figure 13. The first step is the 3D database building of library products corresponding to the selected chemistry, the available/preselected reagents, and the privileged motif. The pharmacophore keys for the library products are calculated using one key per reagent in the list to optimize. A dynamic ranking of these per reagent pharmacophore keys versus the 7TM-GPCR reference key is performed, with a recalculation of the 7TM-GPCR pharmacophoric space remaining to be covered reference key at each step of the selection. Once a reagent is selected the pharmacophores covered by its corresponding products are eliminated from the reference (to be matched) key. The next reagent is thus selected with this new recalculated reference key to avoid redundancy in pharmacophore coverage, since the objective is to maximize the coverage of the pharmacophore reference key and to minimize the redundancy between products (and libraries). Ugi Library Design Using BPT as Privileged Group. In the given example (see Figure 12), with BPT as privileged group at the amine position in the Ugi chemistry, our goal was to select approximately 20 acids from a list of 42 that were known to work well with the Ugi chemistry. As available aldehydes and isonitriles were not very diverse, it was decided to work with fixed sets of 12 aldehydes and 8 isonitriles chosen to represent the structural diversity of these two reagent categories. Consequently, a total of 4032 compounds were built and grouped per acid in 42 subsets; 42 privileged pharmacophore keys were then calculated and compared to the 7TM-GPCR BPT privileged key. This key was produced based on 502 MDDR compounds from the 7TM-GPCR list (181 compounds with data in the field ACTION, 582 related compounds, and 502 containing BPT motif in MDDR version 95.2). The 7TM-GPCR BPT privileged key calculated using the 4-point pharmacophore definition and 10 distance ranges contained potential pharmacophores. The first 22 ranked acid reagents covered of these pharmacophores (44K from the first reagent set, 11K new from the second; see Figure 14A) and gave a total number of pharmacophores of for the 2112 products ( ). Results from the ranking procedure are given as a spreadsheet that contains the list of sorted reagents with the contribution of their products to the 7TM- GPCR pharmacophoric space needed to be covered and to the total pharmacophoric space. Generally many more pharmacophores than just ones that contain a privileged substructure are exhibited; the total number of pharmacophores exhibited by the library is also calculated. Figure 14 shows typical histograms obtained for these pharmacophore contributions with this procedure. For the new added pharmacophores (histogram A), a large number of pharmacophores occur for the first reagent (the pharmacophores of the first reagent include all the pharmacophores that involve the core structure of the libraries, which are then no longer counted as new for subsequent reagents), followed by a rapid decrease of the number of new pharmacophores for the next reagents (the number of which may vary from one chemistry to another and also depends on the diversity of the reagents). Finally a plateau is reached meaning that very little is added by new reagents in terms of new desired pharmacophores. The second histogram B plots the total number of pharmacophores exhibited by the products for each new selected reagent, and similarly (but inversely) to histogram A after a rapid increase, a plateau is observed. One should note that using a count per pharmacophore, or the additional constraint of a coverage of other properties, would reduce the plateauing and provide further design options. After this first design stage, the typical question what to do next has to be answered. In our case followup or related questions were asked: is it worth using BPT again as the privileged motif, and if yes, in which position or in which chemistry? Thus new library designs were carried out, evaluating the interest in using BPT at the acid position in the Ugi reaction, instead of in the amine. A set of 34 amines was considered, with the goal to select about 20 of them. Pharmacophores already covered by library 1 were removed from the 7TM-GPCR BPT privileged key, the ranking procedure applied to now identify reagents that will cover new pharmacophores not already covered by library 1 (reduce redundancy between libraries). Before finalizing the set of reagents for the library production, other parameters such as molecular weight and clogp of the products were considered. While it is quite easy to gain new pharmacophores and to cover more and more of the 7TM-GPCR BPT pharmacophores with the first libraries, it becomes rapidly very difficult to cover the ones not covered. At this stage in order to go further an analysis of the

11 4-Point Method for Molecular Similarity/Diversity Journal of Medicinal Chemistry, 1999, Vol. 42, No Figure 13. Schematic outline of the library design procedure. Figure 15. Unrepresented pharmacophores (found in 7TM- GPCR BPT set from MDDR) after library production. Figure 14. Contributions of pharmacophores per acid reagent in the order of the selection for optimization in the Ugi reaction with BPT as privileged motif at the amine position. Histogram A: number of new pharmacophores in the BPT 7TM- GPCR pharmacophoric space added by each new selected reagent. Histogram B: increase in the total number of pharmacophores for each new selected reagent. remaining not covered pharmacophore keys can be carried out, taking advantage of the pharmacophore method where each descriptor corresponds to a real 3D pharmacophoric shape that can be analyzed in a general way (e.g., number of acids and bases, what distances,...) or specific way (e.g., superposed on a molecule exhibiting it). As an example, a by feature analysis performed on the remaining not covered pharmacophores after production of several libraries is shown in Figure 15 (key exported from Chem-X and analyzed using in-house programs). This indicates clearly that a large part of the missing pharmacophores (36%) contained a combination of acids and bases or a combination involving an acid (23%) or a base (22%). Even if the relative absence of such features in our produced libraries was known, it was useful to quantify it at this stage. It was also found useful to rank the MDDR 7TM-GPCR molecules containing BPT against the key of the not covered pharmacophores and visualize them. This led to the design of further Ugi libraries with reagents containing tert-butyl ester-protected acids or BOC-protected amines to produce products exhibiting free acids and bases after a deprotection stage at the end of the synthesis. These libraries added significant new and previously missing

12 3262 Journal of Medicinal Chemistry, 1999, Vol. 42, No. 17 Mason et al. Figure 16. Sum total number of 4-point pharmacophores from consecutive 14K sets of Ugi libraries designed for 7TM- GPCR targets. pharmacophores relative to earlier libraries. Figure 16 illustrates the progression of new pharmacophores explored with Ugi libraries, analyzed in sets of 14K compounds. It can be seen that it was possible to triple the total number of potential pharmacophores expressed by the first set (Lib1) with consecutive libraries (Lib2 and Lib3) that were designed to maximize the filling of not covered pharmacophores from existing libraries and included the use of many more reagents that would give free acids and bases in the products (Lib3 group particularly). Conclusion The 4-point 3D multiple potential pharmacophore methods implemented and customized as described in this paper provide a new method to measure the similarity and diversity of sets of compounds. A new multipharmacophoric similarity measure has been developed that can be applied to both ligand-ligand (e.g., for molecules required to share a similar biological activity) and ligand-receptor interactions. In particular a new relative measure of similarity and diversity was introduced using a subset of pharmacophoric shapes that contain a special feature, such as a privileged substructure. This provides a new approach to library design, and procedures and examples for 7TM-GPCR targets have been presented. A useful aspect of this method, multiple pharmacophores containing a special feature, is the ability to focus on relevant information, here being the pharmacophoric shape of the compounds relative to a specific substructure or motif. A consistent frame of reference, independent of alignment of particular conformations, is obtained for comparing molecules, databases of molecules, and complementarity to a protein site whether considering all pharmacophoric shapes or just those containing a special feature. Experimental Section Computational Studies. Calculations were performed using the Chem-X/ChemDiverse software (Jan97 and Apr97 releases) including the pre-release 4-center pharmacophore module (available for general release from the Jul97 release). Customized scripts (PCL) of Chem-X commands were used for this work. A customized parametrization file and database were used, using the special atom types described in Table 2; algorithmic perception of acidic and basic centers was deactivated, and instead fragments in the parametrization database were used to identify the relevant environments and assign atom types with the Chem-X center numbers for basic and acidic features (basic ) 1,2,3 amines, amidines, guanidines; acidic ) carboxylic, tetrazolic, acyl sulfonamides). Pharmacophore Features and Atom Types. The methods used to assign the atom types have been discussed elsewhere 2,5,14 and are based on an order-dependent database of fragments. Either a total connectivity or a bond-orderdependent (with minimum connectivity defined) substructural fragment match is used to assign atom types. The sequential use of the fragments enables atom type assignments by fragment stored later in the database to override (and see) modifications made by earlier fragments. Very specific definitions (e.g., generic case first, then special cases) and the addition of unique atom type identifiers for special groups or features as for the privileged substructures discussed below can thus be achieved. Hydrophobic regions are identified in this work using the bond polarity method in Chem-X to add dummy atoms, together with some specific fragments such as isopropyl that force a hydrophobic feature to be assigned, regardless of the environment. Alternatively, or additionally, hydrophobic dummy atoms can be systematically added and evaluated for a polar environment using the electrostatic potential, retaining only those in a nonpolar environment. 2 The correct identification of acidic and basic groups was considered to be crucial and was done explicitly through the customized parametrization database to define specific atom types rather than by using the default algorithmic method available in Chem-X. For the work reported in this paper, acids and bases (defined as groups expected to be ionized at physiological ph) were not assigned to hydrogen bond acceptors and donors but kept separately as acidic/basic features. In other studies it could be useful to allow acids/bases to be also hydrogen bond acceptors/donors, and this is readily done by also assigning these features to the corresponding atom types for acids and bases. Distance Ranges and Pharmacophore Keys. The distances between pharmacophoric features for all the evaluated conformers are calculated exactly but stored using a binning scheme, with each distance represented by the bin into whose range it falls. A customized distance range file was used, see Table 4, with the default ChemDiverse range extended from 15 to Å and new nonlinear distance ranges. Three distances are needed to characterize each 3-point pharmacophore (triangle), while there are six distances needed to describe each pharmacophore for the 4-point method (tetrahedron). To store all the combinations of features and distances, a pharmacophore key is used. It is a bit string where each bit represents a geometrically valid combination of features and 3D distances (see Figure 2). The geometrical validity is checked by applying the triangle inequality rule where one distance cannot be longer than the sum of the two others. This removes about one half of the theoretical combinations for the 3-point pharmacophores. One of our requirements for the extension of ChemDiverse to 4-point pharmacophores commissioned with Chemical Design was the ability to flexibly use customized distance ranges and/or combinations of features. 27 On the basis of our experience with 3D database searching, and taking into account the torsional increments used in the conformational sampling, customized distance ranges were defined for both 3- and 4-point calculations. Longer distances were included, and the size of each range was varied according to a fixed percentage variation from the distance middle point value (e.g., (15%), such that larger distances had larger ranges. A maximum of 16 ranges for the 3-point method and 10 or 7 ranges for the 4-point method were normally used; this contrasts with the default ChemDiverse ranges (between 2 and 15 Å) of 15 for 4-point (1 Å interval plus additional ranges for distances less than or greater than the defined limits) and 32 for 3-point (0.1-1 Å interval). The use of larger distance bin sizes reduces the risk of failing to set a bit in the pharmacophore key because of conformational sampling limitations (e.g., the size of torsional increment for flexible bonds) and produces keys of a much more manageable size (with 7 features and 13 distance ranges the 4-point pharmacophore key size is 12MB, reducing to 3MB with 10 distance ranges and to 0.7MB with 7 distance ranges). Details of the customized ranges are given in Table 4, and Table 5 gives the total number of hypothetical pharmacophores for different combinations. The key size for 4-point keys was about 125KB/million hypothetical pharmacophores. Figure 3