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Molecular Diversity (2005) 9: 131–139 c Springer 2005 Full-length paper Rational selection of structurally diverse natural product scaffolds with favorable ADME properties for drug discovery D.S. Samiulla1,† , V.V. Vaidyanathan1,† , P.C. Arun1 , G. Balan1 , M. Blaze1 , S. Bondre1 , G. Chandrasekhar1 , A. Gadakh1 , R. Kumar1 , G. Kharvi1 , H.-O. Kim2 , S. Kumar1 , J.A. Malikayil1 , M. Moger1 , M.K. Mone1 , P. Nagarjuna1 , C. Ogbu2 , D. Pendhalkar1 , A.V.S. Raja Rao1 , G. Venkateshwar Rao1 , V.K. Sarma1 , S. Shaik1 , G.V.R. Sharma1 , S. Singh1 , C. Sreedhar1 , R. Sonawane1 , U. Timmanna1 & L.W. Hardy2,∗ 1Aurigene Discovery Technologies, Ltd., Electronic City Phase 2, Hosur Road, Bangalore-562158, India; 2 Aurigene Discovery Technologies, Inc., 99 Hayden Avenue, Lexington, Massachusetts 02420, U.S.A. (∗Author for correspondence, E-mail: [email protected], Tel: 508-347-5320, Fax: 978-685-3734) Received 16 March 2004; Accepted 14 May 2004 Key words: computational design, early ADME, lead-like, metabolic stability, natural products, permeability, scaffold Summary Natural product analogs are significant sources for therapeutic agents. To capitalize efficiently on the effective features of naturally occurring substances, a natural product-based library production platform has been devised at Aurigene for drug lead discovery. This approach combines the attractive biological and physicochemical properties of natural product scaffolds, provided by eons of natural selection, with the chemical diversity available from parallel synthetic methods. Virtual property analysis, using computational methods described here, guides the selection of a set of natural product scaffolds that are both structurally diverse and likely to have favorable pharmacokinetic properties. The experimental characterization of several in vitro ADME properties of twenty of these scaffolds, and of a small set of designed congeners based upon one scaffold, is also described. These data confirm that most of the scaffolds and the designed library members have properties favorable to their utilization for creating libraries of lead-like molecules. Abbreviations: ADME, absorption-distribution-metabolism-excretion; BCUT, Burden-Chemical Abstract Services – University of Texas; DMSO, dimethylsulfoxide; e-ADME, early ADME; HPLC/UV, high performance liquid chromatography monitored by ultraviolet absorbance; LC-MS, liquid chromatography–mass spectrometry; MDCK, Madin-Darby canine kidney; NCI, National Cancer Institute; PBS, phosphate buffered saline; PSA, polar surface area; TEER, transepithelial electrical resistance Introduction Natural products have historically provided important and effective therapeutic agents [1]. Aspirin, penicillin, and paclitaxel are just three of numerous well-known natural products used to improve human health. Moreover, analogs of such natural products as benzylpenicillin and semisynthetic drugs, such as taxotere, have proven to be enormously fruitful for new drug discovery. Recent studies have shown that natural compounds and their analogs continue to be major sources of new drugs [2, 3]. Collections of natural products exhibit physicochemical property profiles that are favorable compared to those of drugs and complementary to those † These authors have contributed equally to this work. provided by synthetic compounds that derive from combinatorial chemistry [4, 5]. Despite these advantages, the classical processes to identify discrete new chemical entities from natural product sources are too inefficient to have survived in many of the current discovery environments at pharmaceutical companies. However, interest in natural products and their analogs as sources of pharmaceutical agents has shown recent resilience [6–8]. To capitalize more efficiently on the effective features of naturally occurring substances, we have adopted a natural product-based library production platform for lead discovery. This approach combines the attractive biological and physicochemical properties of natural product scaffolds, provided 132 by eons of natural selection, with the chemical diversity available from parallel synthetic methods. The selection of natural product scaffolds is guided by several criteria. Three major criteria are: (1) computational analyses of virtual properties calculated from the structures of the small molecule scaffolds alone, (2) the density of patented entities in “chemical space” around the each scaffold being considered, and (3) accessibility (by either total synthesis or extraction from a renewable source). The virtual property analysis considers both the diversity for the scaffolds set as a whole (e.g., how well the scaffolds complement each other in “chemical diversity”) and the structural diversity accessible for each particular scaffold (either by total synthesis of analogs or by derivatization of a scaffold with multiple potential positions for modification). Further, the virtual property assessment includes a consideration of the “lead-like” nature of the scaffolds, so that a good starting point exists for lead generation and optimization. The goal of this process is to define a scaffold set that is structurally diverse and contains members likely to have favorable pharmacokinetic properties. At present, fifty-one natural product scaffolds have been selected, using the process broadly outlined above, for further exploration. Three examples are cryptoheptine, vasicinone, and Z-guggulsterone (Figure 1A). Z-Guggulsterone, the active constituent of an ayurvedic medicine used in India for several thousand years, is especially exciting. ZGuggulsterone has clinically proven benefits in the treatment of human hyperlipidemia, and is approved and marketed for that purpose in India [9]. It was recently proven, using gene knockout methods, that Z-guggulsterone targets the farnesoid X receptor (also known as the bile acid receptor) in its effects on cholesterol metabolism in the mouse [10]. The production of small molecule libraries based on these natural product scaffolds is cost-effective, efficient, expedient, and amenable to solid-phase and parallel solutionphase synthesis. For example, a small library of vasicinone analogs was chosen for synthesis from a virtual library to provide a structurally diverse set with acceptable ADME parameters. Several members of the vasicinone library are shown in Figure 1B 1 . The computational analysis of the vasicinone library is described here. For target-driven drug discovery, the designs of small molecule libraries based on the natural product scaffolds are ideally guided not by diversity metrics primarily, but by the atomic structures of the targets. Design of our targeted libraries is based both on in silico estimates of the ADME properties of the virtual libraries calculated solely from the small molecule structures and by computational screening of virtual libraries against structural models of specific proteins that are targeted for therapeutic discovery. Major reasons for the failure of early drug candidates to reach the market include inappropriate ADME (absorption, distribution, metabolism and excretion) properties of the candidates, and metabolism-based adverse interactions of the candidates with existing drugs. By some estimates, 40–50% of new chemical entities in the drug discovery pipeline fail due to poor ADME properties [11, 12]. From a commercial perspective, poorly behaved compounds need to be recognized and removed as early as possible in the discovery process, rather than during the more costly clinical testing and development phase. To speed the provision of the data needed for the design and discovery of well-behaved drug candidates, “early ADME” (e-ADME) assays have been developed [13–15]. These assays use in vitro surrogates for in vivo (A) (B) Figure 1. (A) Representative natural product scaffolds, (B) Representative analogs of vasicinone. 133 physiology, and allow information about ADME and metabolism-based adverse drug-drug interactions to be incorporated early in drug discovery. The e-ADME parameters of twenty of our natural product scaffolds have been characterized. The properties studied were aqueous solubility at pH 7.4, inhibition of several recombinant human cytochrome P450 isozymes, stability to exposure to pooled human hepatic microsomes, and permeability across the MDCK (Madin-Darby Canine Kidney) cell monolayer. Incorporation of early ADME assessments in our drug discovery projects has helped us to identify natural product scaffolds and natural product analogs with high probability of good intestinal absorption and to flag potential problems arising from hepatic metabolism. This approach allows potential problems with poor absorption or metabolic interactions to be avoided by re-design of analogs at the discovery stage in a cost-effective way. Methods the various structural fragments present were calculated [19]. The Tanimoto similarity index was defined as: T ( f 1 , f 2 ) = N1&2 /N1|2 where, f1 = fingerprint 1, f2 = fingerprint 2, N1&2 = number of fields present in both f1 and f2, N1|2 = number of fields present in either f1 or f2. A variety of structure-based parameters, relevant to ADME characteristics such as those described by Lipinski [20] and by Veber and co-workers [21], were also evaluated for the compounds. The parameters calculated were ClogP (calculated using Sybyl from Tripos), molecular weight and number of rotatable bonds (assessed manually), and number of hydrogen bond donors and acceptors (assessed using ChemDraw for Excel). In the calculation of the number of rotatable bonds, amide bonds are not counted, since the rotational barrier should prevent free rotation. The polar surface area (PSA) was calculated using the MolCad module of Sybyl. Computational property and diversity analyses General analytical methods The BCUT parameter approach, pioneered by Pearlman [16], was employed as provided in the software package from Tripos [17]. To establish statistically valid orthogonal axes of the chemistry vector space to use for the diversity analysis of natural product scaffolds, BCUT parameters were generated for about 25,000 molecules from the National Cancer Institute (NCI) database, as provided by Tripos. Correlation between the axes was calculated and a chemistry space was defined with minimal r 2 (<0.25) values between the axes. The diversity of scaffold molecular structures was compared in this chemistry space based on partitioning (cell based) methods. Evaluation of the vasicinone library for diversity was done differently from the evaluation of the scaffolds. Relative diversity is important for a library with respect to the scaffold [18]. Therefore, the BCUTs for the members of the vasicinone library were generated. Chemistry space definitions were generated which maintained the r 2 (between the axes) less than 0.25. As the number of library members was low, it would not be statistically valid to assume the axis to be orthogonal. Hence, the same BCUTs were generated for the NCI set of molecules, and r 2 between the axes were evaluated. The chemistry space with r 2 (between the axes) less than 0.25 was then accepted, and was partitioned into cells such that the occupancy was 14.8%. Statistics of occupancy for the compounds were calculated. The chemistry spaces in both the cases are described as: two partial charges based BCUTs, one H-donor based BCUT, one H- acceptor based BCUT, and two polarizability based BCUTs. A molecular fingerprint-based analysis was also performed for both the scaffolds and the vasicinone library members, using 992 bit Unity 2D fingerprint definitions. The fingerprints were generated, and the correlations between Fluorescence measurements were done in 96-well plates using a Spectromax Gemini XS reader (Molecular Devices, USA). Transepithelial resistance (TEER) measurements were made using a World Precision Instrument (Florida, USA) probe. HPLC analyses (performed on an Agilent Technologies 1100 series system) were done using a reverse phase column (Zorbax Eclipse XDB C18, 150 × 4.6 mm, 3.5 micron), with quantitation based on UV absorbance at 254 nm. The LC-MS analyses were performed in multiple reaction monitoring mode using an Applied Biosystems API 2000, fed by a in-line Agilent Technologies 1100 series HPLC using the same model of Zorbax reverse phase column for sample chromatography. Aqueous solubility assay DMSO solutions of test compounds were added to phosphate buffered saline pH 7.4 (PBS) in a 96 deep-well plate to generate theoretical concentrations of 200 µM in 2% DMSO. The solutions were equilibrated by shaking (200 rpm, Ika plate shaker) for 16 h at 25 ◦ C. Undissolved compound was removed by centrifugation, and the supernatant was analyzed by HPLC-UV or LC/MS/MS. Aqueous solubility was calculated using the equation: Aqueous solubility = 200 µM × PAPBS /PADMSO where PAPBS and PADMSO are the peak areas from the analyses of test compound in PBS with 2% DMSO and of test compound in 100% DMSO, respectively. Assays were performed in duplicate or triplicate. 134 MDCK monolayer permeability assay Madin-Darby Canine Kidney (MDCK) cells (American Type Culture Collection, Manassas, Virginia, USA) were used to assess compound permeability [22]. The cells were grown in Dulbecco’s modified Eagle medium supplemented with 10% fetal bovine serum, 1 mM non-essential amino acids, 1 mM sodium pyruvate and gentamicin sulphate (50 µg/mL) to 70– 80% confluency prior to seeding in 24-well plates loaded with polycarbonate Millicell inserts (Millipore, 12 mm diameter, 0.4 µm, 50,000 cells/insert). Cells were fed with fresh medium every other day and incubated at 37 ◦ C, with 5% CO2 for 3 days prior to drug transport experiment. Cell monolayer integrity was assessed by measuring TEER values. Drugs were applied at 50 µM in Hank’s buffered salt solution containing 0.5% DMSO to the apical chamber and the transport assay was carried out for 2 h at 37 ◦ C. At the end of the assay, samples from both apical and basal chambers were collected for analysis by HPLC/UV or LC/MS/MS, and the monolayer integrity was re-assessed by dye rejection using Lucifer Yellow. Data from wells with dye rejection less than 98% were rejected. Apparent permeability (Papp ) values were calculated using the equation: BD Gentest): 7-methoxy-4-(trifluoromethyl) coumarin for CYP2C9, 3[2-N,N-diethyl-N-methylammonium)ethyl]-7methoxy-4-methylcoumarin for CYP2D6, and 7-benzyloxy4-(trifluoromethyl)coumarin for CYP3A4. All assays were performed in duplicate. Results and discussion To understand chemical diversity for sets of compounds to be used for chemical biology, that diversity must be quantified in a manner relevant to the biological role for the compounds. The methods used to quantify diversity are “fingerprint” based approaches and “chemistry space” based approaches. In fingerprint-based approaches, bit strings Papp = d Q/dt × 1/Co × 1/A where, dQ/dt = permeability rate in µg/sec, Co = initial concentration in µg/mL, A = membrane surface area (0.6 cm2 for 12 mm inserts), Papp values were expressed in cm/sec. All the assays were performed in triplicate. Human hepatic microsomal stability assay Figure 2. Distribution of the cell occupancy for the vasicinone library members. Pooled human liver microsomes were employed to assess the potential instability of compounds to phase I metabolism [23, 24]. The microsomes (BD Gentest) were incubated with test compounds (1–5 µM in 0.2% DMSO 1 buffer) at 37 ◦ C for 30 min. The reaction was stopped by the addition of acetonitrile containing haloperidol as internal standard. Precipitated protein was removed by centrifugation and the supernatants were analyzed by HPLC-UV or LC/MS/MS method. Stability was assessed by the disappearance of compound based on the change in analyte to internal standard peak height ratio. Metabolic stability was defined as the amount of test compound remaining after the incubation with human hepatic microsomes, and expressed as a percentage of the initial concentration. All assays were performed in triplicate. Human cytochrome P450 inhibition assay Human recombinant Cytochrome P450 isozymes 2C9, 2D6 and 3A4 (BD Gentest) were incubated with 5 µM of test compound in buffer containing 0.1% DMSO for 10 min at 37 ◦ C. The residual enzyme activity was measured [15] using the following fluorogenic substrates (also obtained from Figure 3. Scatter plot of molecular weight, ClogP, and PSA for natural product scaffolds. 135 (molecular fingerprints) that indicate the presence of substructural features in each molecule are employed. Similarity and dissimilarity between two molecules are measured as the inner product of these bit string vectors, or a closely related quantity called Tanimoto similarity/dissimilarity indices [19]. This kind of approach is helpful in designing combinatorial libraries as it checks for presence of different kinds of fragments. However, with a collection of natural product scaffolds, this method is not relevant for defining a library, which is diverse in a general sense. The chemistry space approach relies on the definition of a set of quantifiable chemical descriptors, corresponding to dimensions within chemistry space. As the position of a particle in three dimensions is defined by its x-, y-, and z-co-ordinates, the positions of molecules in N-dimensional chemical space are defined by N chemical descriptors. A similar approach was exemplified by Pearlman and co-workers in their analysis of acetylcholinesterase inhibitors [16]. The axes of the chemistry space are BCUTs, which are eigenvalues of the connectivity matrices of the molecular graphs with some properties as the diagonal elements. Pearlman and co-workers found that three of the six BCUT metrics, they had identified from an analysis of a database of 60,000 ‘druglike’ compounds, were receptor relevant, i.e. the actives were clustered in these dimensions. We have employed this same approach to identify scaffolds that are diverse in a biologically relevant chemical space, as defined by the diversity in the compounds in the NCI database. The natural product scaffolds are diverse in the chemical space, since the different scaffolds are found in different cells. Figure 4. Scatter plot of number of rotatable bonds, H bond acceptors, and H bond donors for natural product scaffolds. Figure 6. Scatter plot of number of rotatable bonds, H bond donors, and H bond acceptors for the vasicinone library. Figure 5. Scatter plot of molecular weight, ClogP, and PSA for the vasicinone library. Figure 7. Aqueous solubility of standard drugs in solubility assay correlated with published data. 136 As the distribution of partial charges, polarizability, and Hbonding properties of the structures are mapped by BCUTs based upon them, these scaffolds are diverse in the distribution of these properties. The variety of distribution of these properties in the natural product scaffolds gives them a better chance to be selected by varied types of receptors. The bits in the fingerprints indicate different structural fragments present in the scaffolds. The fact that only 62 bits were highly correlated out of 992 indicates that these scaffolds have different chemical cores in them. Thus, the fingerprints based approach indicates a statistically significant difference in the types of chemical classes present in the scaffolds. This conforms to the chemically intuitive definition of the diversity of natural product scaffolds [4, 5]. Not surprisingly, the vasicinone library was considerably less diverse than the Aurigene collection of natural product scaffolds, with multiple compounds in many cells (cf., Figure 2). As shown in Figures 3 and 4, the Lipinski parameters for the natural product scaffolds satisfy “the rule of five” limits [20]. The number of rotatable bonds is less than seven for all the molecules, which is well within the maximum of 10 suggested for good bioavailability [21]. The maximum PSA for these scaffolds is 310 Å2 . The acceptable maximum fragment-based PSA is 140 Å2 , compounds with PSA values above this limit are expected to have low oral bioavailability [14]. In our experience, the PSA calculated Figure 8. Permeability in MDCK model system correlates well with known human absorption data for orally dosed drugs. Figure 9. Cytochrome P450 inhibition assay with standard inhibitors. 137 Table 1. ADME properties of natural product scaffolds Solubility at pH 7.4, 10−6 M Permeability through MDCK monolayer (Papp ), 10−6 cm/s (±SD) 1 (Vasicinone) >200 74 (±3) 2 (Cryptoheptine) insoluble nd 3 40 6.5 (±0.8) 4 89.7 60 (±2.4) 5 196.2 7.5 (±0.2) 6 (Z-Guggulsterone) 94 33.1 (±0.7) 7 195.9 68 (±4) 8 >200 83 (±3) 9 1.9 nd 10 191 19.1 (±0.4) 11 80 79 (±7) 12 >200 62 (±4) 13 197.5 113 (±8) 14 8.7 70.9 (±0.1) 15 >200 69 (±8) 16 6.1 nd 17 178.5 57 (±3) 18 >200 5.9 (±0.5) 19 >200 46 (±4) 20 13.5 10.5 (±0.8) Scaffold Values are mean (±SD) of triplicate assays. Green = Good, Blue = Acceptable, Red = May need to be modified by analog synthesis. nd = Not determined. using the MolCad method with the energy minimized structures (as implemented by Tripos) is 2.5 times the value calculated by the faster fragment-based approach [26]. Therefore, the acceptable limit for the PSA value computed with MolCad is 350 Å2 . Thus, there are no computational alerts for these molecules for solubility, permeability, and oral bioavailability. Distributions of the ADME relevant computed properties for the vasicinone library are shown in Figures 5 and 6. Among the 172 molecules in the vasicinone library, there are five molecules with molecular weight greater than 500, and one molecule has a ClogP value greater than 5. The MolCad computed PSA has a maximum value of 311 Å2 for the library. The maximum values observed for the numbers of H-Bond donors and H-Bond acceptors are 4 and 6, respectively. The maximum number of rotatable bonds for the any member of the library is 10. Therefore, six molecules have computational alerts. The experimental assays for the e-ADME parameters were validated using drugs and other well characterized compounds with known values for solubility (Figure 7), permeability (Figure 8), inhibitory potency toward cytochrome P450s (Figure 9), and metabolic stability (Figure 10). These validated in vitro assays were then used to assess the eADME parameters (solubility, permeability, inhibition of cytochrome P450 isozymes, and hepatic microsomal stability) for twenty of the natural product scaffolds, and for a subset of compounds in the vasicinone library. Reference compounds with low, medium, and high values for the assayed property are routinely included during the assessments of test compounds to assure the acceptable performance of these assays. Most of the scaffolds and vasicinone library members had acceptable or good aqueous solubility and permeability parameters (Tables 1 and 2), which supports the accuracy of the favorable computational estimates of their physicochemical properties. The majority of the natural product scaffolds have excellent microsomal stability, with more than 50% of the parent compound surviving the 30-min exposure for 18 of the 20 scaffolds examined (Figure 11). Most of the natural product scaffolds and vasicinone analogs also have acceptably weak or moderate inhibitory potencies towards the three cytochrome P450 isozymes that were assayed (Table 2 and Figure 12), which are the prevalent hepatic P450 isozymes in Table 2. ADME properties of Vasicinone analogs Solubility, 10−6 M Papp, 10−6 cm/s 3A4 % inhibition 2C9 % inhibition 2D6 % inhibition 1 >200 69 (±8) 34 43 14 2 >200 87 (±4) 34 26 33 3 >200 67 (±2) 32 25 16 4 >200 75 (±4) 30 27 19 5 >200 46 (±2) 31 22 24 6 >200 65 (±2) 31 32 6 7 >200 64 (±17) 31 29 19 8 >200 73 (±5) 31 36 23 9 >200 35 (±2) 49 66 16 10 >200 2.2 (±0.6) 31 44 7 11 11.6 nd 32 32 17 12 >200 97 (±1) 26 36 24 Vasicinone analogs Green = Good, Blue = Acceptable, Red = requires modification by analog synthesis. nd = Not determined. 138 Figure 10. Microsomal stability assay with standard compounds. Figure 11. Microsomal stability of natural products scaffolds. Figure 12. Inhibition of CYP450 isozymes by natural product scaffolds. 139 most humans [27]. Generally, the natural product scaffolds have good e-ADME properties. This strongly supports their suitability for library generation for drug discovery. Preservation of the favorable e-ADME properties during library buildouts starting with the scaffolds is suggested by the good e-ADME properties exhibited by the vasicinone library members. A few scaffolds have marginal e-ADME properties, indicating that if they are pursued, these properties will likely require modification during library build-outs. In summary, the computational and experimental assessments of the properties of the natural product scaffolds are in agreement. These findings support the utility of the rational process that we have established to choose scaffolds for hit and lead generation libraries for drug discovery. Computational methods clearly have great utility to combine good physicochemical properties (and thereby increase the likelihood of favorable bioavailability) and structural diversity in libraries of natural product analogs. 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