Parameterization and conformational sampling effects in pharmacophore multiplet searching.

Pharmacophore patterns in ligands can be effectively characterized in terms of their constituent pharmacophore multiplets. Bitsets (fingerprints) encoding which particular multiplets are found in a given ligand have been and continue to be used as molecular descriptors in a range of molecular modeling applications, from ligand alignment and diversity analysis to pharmacophore-based flexible searching. Being able to create, store, and manipulate multiplets in compressed form - as bitmaps - has made it possible to integrate them into high-throughput technologies. A number of key parameters affect how well multiplets perform, including the granularity of edge length binning; how different multiplets are weighted in creating hypotheses from multiple ligands; and the number of bits that should be included in a pharmacophore hypothesis. The similarity metric employed for bitmap comparisons also affects search performance, as does the conformational sampling regime used for characterizing flexible molecules. In this report we explore the effect of parameter variation on within- and between-class similarity across seven different pharmacological classes and introduce a new measure of molecular similarity - the asymmetric stochastic cosine - uniquely suited to searching a database for matches to query hypotheses deduced from multiple ligands. Surprisingly, it turns out that the most discriminating bitmaps are obtained using relatively few conformers. The extreme discrimination power seen for single conformers, however, seems to reflect consistent effects of 2D connectivity on the 3D structure obtained. Conformational sampling by systematic search reinforces such circumstantial discrimination and should be avoided. The potential for systematic bias becomes clear when the behavior of otherwise similar conformational ensembles created by local energy minimization or by random sampling is considered. Consolidating information from multiple known actives or establishing single "bioactive" conformations a priori are safer ways to improve discrimination in pharmacophoric multiplet searching.

GALAHAD: 1. pharmacophore identification by hypermolecular alignment of ligands in 3D.

Alignment of multiple ligands based on shared pharmacophoric and pharmacosteric features is a long-recognized challenge in drug discovery and development. This is particularly true when the spatial overlap between structures is incomplete, in which case no good template molecule is likely to exist. Pair-wise rigid ligand alignment based on linear assignment (the LAMDA algorithm) has the potential to address this problem (Richmond et al. in J Mol Graph Model 23:199-209, 2004). Here we present the version of LAMDA embodied in the GALAHAD program, which carries out multi-way alignments by iterative construction of hypermolecules that retain the aggregate as well as the individual attributes of the ligands. We have also generalized the cost function from being purely atom-based to being one that operates on ionic, hydrogen bonding, hydrophobic and steric features. Finally, we have added the ability to generate useful partial-match 3D search queries from the hypermolecules obtained. By running frozen conformations through the GALAHAD program, one can utilize the extended version of LAMDA to generate pharmacophores and pharmacosteres that agree well with crystal structure alignments for a range of literature datasets, with minor adjustments of the default parameters generating even better models. Allowing for inclusion of partial match constraints in the queries yields pharmacophores that are consistently a superset of full-match pharmacophores identified in previous analyses, with the additional features representing points of potentially beneficial interaction with the target.

The effects of biasing torsional mutations in a conformational GA.

This paper describes the effects of incorporating torsional bias into a conformational Genetic Algorithm (GA) such as that found in the GASP program. Several major conclusions can be drawn. Biasing torsional angles toward values associated with local energy minima increases the rate of convergence of the fitness function (consisting of energy, steric, and pharmacophoric compatibility terms) for a set of molecules, but a definite tradeoff exists between total model energy and the steric and pharmacophoric compatibility terms in the fitness score. Biasing torsions in favor of sets of angles drawn from low-energy conformations does not guarantee low total energy, but biased torsional sampling does generally produce less strained models than does the uniform torsional sampling in classical GASP. Overall, torsionally biased sampling produces good models comprised of energetically favorable ligand conformations.

Structural unit analysis identifies lead series and facilitates scaffold hopping in combinatorial chemistry.

Combinatorial chemistry and high-throughput screening technologies produce huge amounts of data on a regular basis. Sieving through these libraries of compounds and their associated assay data to identify appropriate series for follow-up is a daunting task, which has created a need for computational techniques that can find coherent islands of structure-activity relationships in this sea. Structural unit analysis (SUA) examines an entire data set so as to identify the molecular substructures or fragments that distinguish compounds with high activity from those with average activity. The algorithm is iterative and follows set heuristics in order to generate the structural units. It produces graphs that represent a set of units, which become SUA rules. Finding all of the input structures that match these graphs generates clusters. The Apriori algorithm for association rule mining is adapted to explore all of the combinations of structural units that define useful series. User-defined constraints are applied toward series selection and the refinement of rules. The significance of a series is determined by applying statistical methods appropriate to each data set. Application to the NCI-H23 (DTP Human Tumor Cell Line Screen) database serves to illustrate the process by which structural series are identified. An application of the method to scaffold hopping is then discussed in connection with proprietary screening data from a lead optimization project directed toward the treatment of respiratory tract infections at Bayer Healthcare. SUA was able to successfully identify promising alternative core structures in addition to identifying compounds with above-average activity and selectivity.

Modelling in vitro hepatotoxicity using molecular interaction fields and SIMCA.

There is currently a great deal of interest in creating computational tools for predicting the pharmacological properties of drug development candidates, ranging from physicochemical properties such as pK(a) and solubility to more complex biological properties such as oral bioavailability and toxicity. The limiting factor in many cases is a shortage of good data from which to construct training sets. In other cases, large amounts of data are available, but they use surrogate end-points or are comprised of compounds very different from those usually encountered in drug discovery and development. In such cases large training sets and global models are not necessarily better than local models based on smaller data sets. Such considerations make it as important to examine the available data carefully so as to avoid over-interpretation of the models obtained as it is to minimise errors in prediction per se. The kinds of complications likely to be encountered for in vitro hepatotoxicity modelling are discussed in general terms and illustrated in particular by SIMCA analysis of data obtained from assays of cultured hepatocytes for a large, structurally diverse data set and a smaller, much more focussed one.

Highly flexible ligand binding pocket of ecdysone receptor: a single amino acid change leads to discrimination between two groups of nonsteroidal ecdysone agonists.

The insect steroid hormone 20-hydroxyecdysone works through a ligand-activated nuclear receptor, the ecdysone receptor (EcR), which plays critical roles in insect development and reproduction. The EcR has been exploited to develop insecticides to control pests and gene switches for gene regulation. Recently reported crystal structures of the EcR protein show different but partially overlapping binding cavities for ecdysteroid (ECD) and diacylhydrazine (DAH) ligands, providing an explanation for the differential activity of DAH ligands in insects. 1-Aroyl-4-(arylamino)-1,2,3,4-tetrahydroquinoline (THQ) ligands were recently discovered as ecdysone agonists. Mutagenesis of the EcR (from Choristoneura fumiferana, CfEcR) ligand binding domain followed by screening in a reporter assay led to the identification of CfEcR mutants, which responded well to THQ ligands but poorly to both ECD and DAH ligands. These mutants were further improved by introducing a second mutation, A110P, which was previously reported to cause ECD insensitivity. Testing of these V128F/A110P and V128Y/A110P mutants in a C57BL/6 mouse model coactivator interaction assay and in insect cells showed that this mutant EcR is activated by THQ ligands but not by ECD or DAH ligands. The CfEcR and its V128F/A110P mutant were used to demonstrate simultaneous regulation of two reporter genes using THQ and DAH ligands.

Predicting drug pharmacokinetic properties using molecular interaction fields and SIMCA.

We have developed a method that combines molecular interaction fields with soft independent modeling of class analogy (SIMCA) to predict pharmacokinetic drug properties. Several additional considerations to those made in traditional QSAR are required in order to develop a successful QSPR strategy that is capable of accommodating the many complex factors that contribute to key pharmacokinetic properties such as ADME (absorption, distribution, metabolism, and excretion) and toxicology. An accurate prediction of oral bioavailability, for example, requires that absorption and first-pass hepatic elimination both be taken into consideration. To accomplish this, general properties of molecules must be related to their solubility and ability to penetrate biological membranes, and specific features must be related to their particular metabolic and toxicological profiles. Here we describe a method, which is applicable to structurally diverse data sets while utilizing as much detailed structural information as possible. We address the issue of the molecular alignment of a structurally diverse set of compounds using idiotropic field orientation (IFO), a generalization of inertial field orientation. We have developed a second flavor of this method, which directly incorporates electrostatics into the molecular alignment. Both variations of IFO produce a characteristic orientation for each structure and the corresponding molecular fields can then be analyzed using SIMCA. Models are presented for human intestinal absorption, blood-brain barrier penetration and bioavailability to demonstrate ways in which this tool can be used early in the drug development process to identify leads likely to exhibit poor pharmacokinetic behavior in pre-clinical studies, and we have explored the influence of conformation and molecular field type on the statistical properties of the models obtained.

Molecular design and bioavailability.

A "snapshot" of current medicinal chemistry work on bioavailability is drawn from issues of J. Med. Chem. covering the time period between September 2001 and September 2002. An exhaustive compilation of reported absolute oral bioavailability (F) values for this period is included, covering 34 structural series and 107 distinct compounds, with data for multiple species in many cases. This is supplemented with a discussion of more qualitative oral bioavailability results, and with illustrative examples addressing clearance, prodrug design, and blood/brain barrier penetration problems. Papers discussing predictions pertaining to one or another aspect of bioavailability are also discussed, and some thoughts on future directions of work on in silico prediction in this area are presented.