Christine Humblet

Escape from flatland: increasing saturation as an approach to improving clinical success.

The medicinal chemistry community has become increasingly aware of the value of tracking calculated physical properties such as molecular weight, topological polar surface area, rotatable bonds, and hydrogen bond donors and acceptors. We hypothesized that the shift to high-throughput synthetic practices over the past decade may be another factor that may predispose molecules to fail by steering discovery efforts toward achiral, aromatic compounds. We have proposed two simple and interpretable measures of the complexity of molecules prepared as potential drug candidates. The first is carbon bond saturation as defined by fraction sp(3) (Fsp(3)) where Fsp(3) = (number of sp(3) hybridized carbons/total carbon count). The second is simply whether a chiral carbon exists in the molecule. We demonstrate that both complexity (as measured by Fsp(3)) and the presence of chiral centers correlate with success as compounds transition from discovery, through clinical testing, to drugs. In an attempt to explain these observations, we further demonstrate that saturation correlates with solubility, an experimental physical property important to success in the drug discovery setting.

Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy

Molecular docking programs are widely used modeling tools for predicting ligand binding modes and structure based virtual screening. In this study, six molecular docking programs (DOCK, FlexX, GLIDE, ICM, PhDOCK, and Surflex) were evaluated using metrics intended to assess docking pose and virtual screening accuracy. Cognate ligand docking to 68 diverse, high-resolution X-ray complexes revealed that ICM, GLIDE, and Surflex generated ligand poses close to the X-ray conformation more often than the other docking programs. GLIDE and Surflex also outperformed the other docking programs when used for virtual screening, based on mean ROC AUC and ROC enrichment values obtained for the 40 protein targets in the Directory of Useful Decoys (DUD). Further analysis uncovered general trends in accuracy that are specific for particular protein families. Modifying basic parameters in the software was shown to have a significant effect on docking and virtual screening results, suggesting that expert knowledge is critical for optimizing the accuracy of these methods.

Enhancing the diversity of a corporate database using chemical database clustering and analysis

SummaryThe contribution that the Chemical Abstracts structural database (CAST-3D) and the Maybridge database (MAY) would make to diversifying the structural information and property space spanned by our corporate database (CBI) is assessed. A subset of the CAST-3D database has been selected to augment the structural diversity of various electronic databases used in computer-assisted drug design projects. The analysis of the MAY database directly offers the potential to expand the CBI compound library, but also provides a source for structural diversity in a format suitable for computer-assisted database searching and molecular design. The analysis performed is twofold. First, a nonhierarchical clustering technique available in the Daylight clustering package is applied to evaluate the structural differences between databases. The comparison is then extended to analyze various structure-derived property spaces calculated from molecular descriptors such as the logarithm of the octanol-water partition coefficient (CLOGP), the molar refractivity (CMR) and the electronic dipole moment (CDM). The diversity contribution of each database to these property spaces is quantified in relation to our corporate database.

Insights for predicting blood-brain barrier penetration of CNS targeted molecules using QSPR approaches.

Due to the high attrition rate of central nervous system drug candidates during clinical trials, the assessment of blood-brain barrier (BBB) penetration in early research is particularly important. A genetic approximation (GA)-based regression model was developed for predicting in vivo blood-brain partitioning data, expressed as logBB (log[brain]/[blood]). The model was built using an in-house data set of 193 compounds assembled from 22 different therapeutic projects. The final model (cross-validated r(2) = 0.72) with five molecular descriptors was selected based on validation using several large internal and external test sets. We demonstrate the potential utility of the model by applying it to a set of literature reported secretase inhibitors. In addition, we describe a rule-based approach for rapid assessment of brain penetration with several simple molecular descriptors.

An investigation of Chromatium vinosum high-potential irondashsulfur protein by EPR and Mossbauer spectroscopy; evidence for a freezing-induced dimerization in NaCl solutions

The high-potential iron-sulfur protein (HiPIP) from Chromatium vinosum contains a cubane prosthetic group that shuttles between the [4Fe-4S]3+,2+ states. We find that the EPR spectra from this protein can be explained as a sum of two components, a major one with g = 2.02; 2.04; 2.12, and a minor one with g = 2.04; 2.07; approximately 2.13. In the presence of 0.1-2.0 M NaCl, freezing induces polymerization of the protein (presumably dimers), which is detected as intercluster spin-spin interaction in the EPR. The observed spin-spin interactions are interpreted as being due to two very similar dimeric structures in an approx. 1:2 ratio. Computer simulation of the X- and Q-band EPR spectra shows that the z-components of the g-tensors in each dimer pair must be co-linear, with center-to-center distances between the clusters of approximately 13 A and approximately 16 A. Inspection of possible dimeric structures of C. vinosum HiPIP by standard molecular graphics procedures revealed that the Fe/S cluster is exposed toward a flattened surface and is accessible to solvent. Moreover, the Fe/S clusters in two HiPIP molecules can easily achieve a center-to-center distance of approximately 14 A when approaching along a common 3-fold axis that extends through the S4 sulfur atom of the cubane; the z-component of the EPR g-tensor is co-linear with this symmetry axis.

The Role of 4-phosphonodifluoromethyl- and 4-phosphono-phenylalanine in the selectivity and cellular uptake of SH2 domain ligands

Incorporation of 4-phosphonodifluoromethyl-phenylalanine (F2Pmp) and 4-phosphono-phenylalanine into SH2 targeted peptides and peptidomimetic ligands was found to effect binding affinity and selectivity of these ligands toward the Src and Abl SH2 domains. Furthermore, dipeptide analogs containing these phosphonate amino acids were used to produce prodrugs with excellent cellular delivery and reconversion rates.

CLIP: Similarity Searching of 3D Databases Using Clique Detection†

This paper describes a program for 3D similarity searching, called CLIP (for Candidate Ligand Identification Program), that uses the Bron-Kerbosch clique detection algorithm to find those structures in a file that have large structures in common with a target structure. Structures are characterized by the geometric arrangement of pharmacophore points and the similarity between two structures calculated using modifications of the Simpson and Tanimoto association coefficients. This modification takes into account the fact that a distance tolerance is required to ensure that pairs of interatomic distances can be regarded as equivalent during the clique-construction stage of the matching algorithm. Experiments with HIV assay data demonstrate the effectiveness and the efficiency of this approach to virtual screening.

Using a homology model of cytochrome P450 2D6 to predict substrate site of metabolism

CYP2D6 is an important enzyme that is involved in first pass metabolism and is responsible for metabolizing ~25% of currently marketed drugs. A homology model of CYP2D6 was built using X-ray structures of ligand-bound CYP2C5 complexes as templates. This homology model was used in docking studies to rationalize and predict the site of metabolism of known CYP2D6 substrates. While the homology model was generally found to be in good agreement with the recently solved apo (ligand-free) X-ray structure of CYP2D6, significant differences between the structures were observed in the B′ and F–G helical region. These structural differences are similar to those observed between ligand-free and ligand-bound structures of other CYPs and suggest that these conformational changes result from induced-fit adaptations upon ligand binding. By docking to the homology model using Glide, it was possible to identify the correct site of metabolism for a set of 16 CYP2D6 substrates 85% of the time when the 5 top scoring poses were examined. On the other hand, docking to the apo CYP2D6 X-ray structure led to a loss in accuracy in predicting the sites of metabolism for many of the CYP2D6 substrates considered in this study. These results demonstrate the importance of describing substrate-induced conformational changes that occur upon binding. The best results were obtained using Glide SP with van der Waals scaling set to 0.8 for both the receptor and ligand atoms. A discussion of putative binding modes that explain the distribution of metabolic sites for substrates, as well as a relationship between the number of metabolic sites and substrate size, are also presented. In addition, analysis of these binding modes enabled us to rationalize the typical hydroxylation and O-demethylation reactions catalyzed by CYP2D6 as well as the less common N-dealkylation.

Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability

High throughput microsomal stability assays have been widely implemented in drug discovery and many companies have accumulated experimental measurements for thousands of compounds. Such datasets have been used to develop in silico models to predict metabolic stability and guide the selection of promising candidates for synthesis. This approach has proven most effective when selecting compounds from proposed virtual libraries prior to synthesis. However, these models are not easily interpretable at the structural level, and thus provide little insight to guide traditional synthetic efforts. We have developed global classification models of rat, mouse and human liver microsomal stability using in-house data. These models were built with FCFP_6 fingerprints using a Naïve Bayesian classifier within Pipeline Pilot. The test sets were correctly classified as stable or unstable with satisfying accuracies of 78, 77 and 75% for rat, human and mouse models, respectively. The prediction confidence was assigned using the Bayesian score to assess the applicability of the models. Using the resulting models, we developed a novel data mining strategy to identify structural features associated with good and bad microsomal stability. We also used this approach to identify structural features which are good for one species but bad for another. With these findings, the structure-metabolism relationships are likely to be understood faster and earlier in drug discovery.

Computation of 3D queries for ROCS based virtual screens

Rapid overlay of chemical structures (ROCS) is a method that aligns molecules based on shape and/or chemical similarity. It is often used in 3D ligand-based virtual screening. Given a query consisting of a single conformation of an active molecule ROCS can generate highly enriched hit lists. Typically the chosen query conformation is a minimum energy structure. Can better enrichment be obtained using conformations other than the minimum energy structure? To answer this question a methodology has been developed called CORAL (COnformational analysis, Rocs ALignment). For a given set of molecule conformations it computes optimized conformations for ROCS screening. It does so by clustering all conformations of a chosen molecule set using pairwise ROCS combo scores. The best representative conformation is that which has the highest average overlap with the rest of the conformations in the cluster. It is these best representative conformations that are then used for virtual screening. CORAL was tested by performing virtual screening experiments with the 40 DUD (Directory of Useful Decoys) data sets. Both CORAL and minimum energy queries were used. The recognition capability of each query was quantified as the area under the ROC curve (AUC). Results show that the CORAL AUC values are on average larger than the minimum energy AUC values. This demonstrates that one can indeed obtain better ROCS enrichments with conformations other than the minimum energy structure. As a result, CORAL analysis can be a valuable first step in virtual screening workflows using ROCS.