Stephen D. Pickett

The impact of aromatic ring count on compound developability: further insights by examining carbo- and hetero-aromatic and -aliphatic ring types.

The impact of carboaromatic, heteroaromatic, carboaliphatic and heteroaliphatic ring counts and fused aromatic ring count on several developability measures (solubility, lipophilicity, protein binding, P450 inhibition and hERG binding) is the topic for this review article. Recent results indicate that increasing ring counts have detrimental effects on developability in the order carboaromatics≫heteroaromatics>carboaliphatics>heteroaliphatics, with heteroaliphatics exerting a beneficial effect in many cases. Increasing aromatic ring count exerts effects on several developability parameters that are lipophilicity- and size-independent, and fused aromatic systems have a beneficial effect relative to their nonfused counterparts. Increasing aromatic ring count has a detrimental effect on human bioavailability parameters, and heteroaromatic ring count (but not other ring counts) has increased over time in marketed oral drugs.

DIVERSITY PROFILING AND DESIGN USING 3D PHARMACOPHORES : PHARMACOPHORE-DERIVED QUERIES (PDQ)

The current interest in combinatorial chemistry for lead generation has necessitated the development of methods for design and evaluation of the diversity of the resultant compound libraries. Such methods also have application in selecting diverse sets of compounds for general screening from corporate databases and in the analysis of large sets of structures to identify common patterns. In this paper we describe a novel methodology for calculating diversity and identifying common features based on the three-point pharmacophores expressed by a compound.1 The method has been implemented within the environment of the Chem-X molecular modeling package (ChemDBS-3D), using a systematic analysis of 3D distance space with three point combinations of six pharmacophoric groups. The strategy used to define the pharmacophores is discussed, including an in-house developed atom type parameterization. The method is compared with the related approach being developed into the ChemDiverse module of Chem-X. Results from an ...

Lead Optimization Using Matched Molecular Pairs: Inclusion of Contextual Information for Enhanced Prediction of hERG Inhibition, Solubility, and Lipophilicity

Previous studies of the analysis of molecular matched pairs (MMPs) have often assumed that the effect of a substructural transformation on a molecular property is independent of the context (i.e., the local structural environment in which that transformation occurs). Experiments with large sets of hERG, solubility, and lipophilicity data demonstrate that the inclusion of contextual information can enhance the predictive power of MMP analyses, with significant trends (both positive and negative) being identified that are not apparent when using conventional, context-independent approaches.

Enhancing the Hit-to-Lead Properties of Lead Optimization Libraries†

In this paper we address several issues in the design of lead optimization libraries. Multipharmacophore descriptors were first developed in the context of designing diverse compound libraries. One reason for favoring such descriptors is the importance of the pharmacophore hypothesis in understanding the interaction of a compound with a protein target. Allied to this is the proposal that sampling over all potential pharmacophores leads to diversity in a biologically relevant space. We present results in support of this argument and also demonstrate that such methods are applicable to the design of focused libraries where the aim is to design the library toward a known lead or leads. This portability is important because it means that the same descriptors can be used for diverse library design, screening set selection, and focused library design, giving a consistent approach. We also examine the question of designing libraries with improved pharmacokinetic properties and show that it is possible to derive simple and rapidly computable descriptors applicable to the prediction of drug transport properties. Furthermore, these can be applied in the context of library design, although it may be necessary to synthesize libraries in a noncombinatorial manner to obtain the best results. To address this problem, we describe a Monte Carlo search procedure that allows the selection of a near-combinatorial subset in which all library members satisfy the design criteria. We present an example from our own work that illustrates how consideration of calculated log P, molecular weight, and polar surface area in the design of a combinatorial library can lead to compounds with improved absorption characteristics as determined by experimental Caco-2 measurements.

The developability of heteroaromatic and heteroaliphatic rings – do some have a better pedigree as potential drug molecules than others?

Aqueous solubility, protein binding and CYP450 inhibition data for compounds containing a variety of heteroaromatic and heteroaliphatic rings were analysed and compared to determine which ring types fared best and worst in these developability screens. The results suggest that certain heterorings are generally more developable than others: how this information may be used and some caveats to be borne in mind are discussed.

Classification of Kinase Inhibitors Using BCUT Descriptors

BCUTs are an interesting class of molecular descriptor which have been proposed for a number of design and QSAR type tasks. It is important to understand what kind of information any particular descriptor encodes and to be able to relate this to the biological properties of the molecules. In this paper we present studies with BCUTs for the classification of ATP site directed kinase inhibitors active against five different protein kinases: three from the serine/threonine family and two from the tyrosine kinase family. In combination with a chemometric method, PLS discriminant analysis, the BCUTs are able to correctly classify the ligands according to their target. A novel class of kinase inhibitors is correctly predicted as inhibitors of the EGFR tyrosine kinase. Comparison with other descriptor types such as two-dimensional fingerprints and three-dimensional pharmacophore-based descriptors allows us to gain an insight into the level of information contained within the BCUTs.

Design Principles for Fragment Libraries: Maximizing the Value of Learnings from Pharma Fragment-Based Drug Discovery (FBDD) Programs for Use in Academia

Fragment-based drug discovery (FBDD) is well suited for discovering both drug leads and chemical probes of protein function; it can cover broad swaths of chemical space and allows the use of creative chemistry. FBDD is widely implemented for lead discovery in industry but is sometimes used less systematically in academia. Design principles and implementation approaches for fragment libraries are continually evolving, and the lack of up-to-date guidance may prevent more effective application of FBDD in academia. This Perspective explores many of the theoretical, practical, and strategic considerations that occur within FBDD programs, including the optimal size, complexity, physicochemical profile, and shape profile of fragments in FBDD libraries, as well as compound storage, evaluation, and screening technologies. This compilation of industry experience in FBDD will hopefully be useful for those pursuing FBDD in academia.

QSAR workbench: automating QSAR modeling to drive compound design

We describe the QSAR Workbench, a system for the building and analysis of QSAR models. The system is built around the Pipeline Pilot workflow tool and provides access to a variety of model building algorithms for both continuous and categorical data. Traditionally models are built on a one by one basis and fully exploring the model space of algorithms and descriptor subsets is a time consuming basis. The QSAR Workbench provides a framework to allow for multiple models to be built over a number of modeling algorithms, descriptor combinations and data splits (training and test sets). Methods to analyze and compare models are provided, enabling the user to select the most appropriate model. The Workbench provides a consistent set of routines for data preparation and chemistry normalization that are also applied for predictions. The Workbench provides a large degree of automation with the ability to publish preconfigured model building workflows for a variety of problem domains, whilst providing experienced users full access to the underlying parameterization if required. Methods are provided to allow for publication of selected models as web services, thus providing integration with the chemistry desktop. We describe the design and implementation of the QSAR Workbench and demonstrate its utility through application to two public domain datasets.

Automated Lead Optimization of MMP-12 Inhibitors Using a Genetic Algorithm.

Traditional lead optimization projects involve long synthesis and testing cycles, favoring extensive structure-activity relationship (SAR) analysis and molecular design steps, in an attempt to limit the number of cycles that a project must run to optimize a development candidate. Microfluidic-based chemistry and biology platforms, with cycle times of minutes rather than weeks, lend themselves to unattended autonomous operation. The bottleneck in the lead optimization process is therefore shifted from synthesis or test to SAR analysis and design. As such, the way is open to an algorithm-directed process, without the need for detailed user data analysis. Here, we present results of two synthesis and screening experiments, undertaken using traditional methodology, to validate a genetic algorithm optimization process for future application to a microfluidic system. The algorithm has several novel features that are important for the intended application. For example, it is robust to missing data and can suggest compounds for retest to ensure reliability of optimization. The algorithm is first validated on a retrospective analysis of an in-house library embedded in a larger virtual array of presumed inactive compounds. In a second, prospective experiment with MMP-12 as the target protein, 140 compounds are submitted for synthesis over 10 cycles of optimization. Comparison is made to the results from the full combinatorial library that was synthesized manually and tested independently. The results show that compounds selected by the algorithm are heavily biased toward the more active regions of the library, while the algorithm is robust to both missing data (compounds where synthesis failed) and inactive compounds. This publication places the full combinatorial library and biological data into the public domain with the intention of advancing research into algorithm-directed lead optimization methods.

Analysis of neighborhood behavior in lead optimization and array design

Neighborhood behavior describes the extent to which small structural changes defined by a molecular descriptor are likely to lead to small property changes. This study evaluates two methods for the quantification of neighborhood behavior: the optimal diagonal method of Patterson et al. and the optimality criterion method of Horvath and Jeandenans. The methods are evaluated using twelve different types of fingerprint (both 2D and 3D) with screening data derived from several lead optimization projects at GlaxoSmithKline. The principal focus of the work is the design of chemical arrays during lead optimization, and the study hence considers not only biological activity but also important drug properties such as metabolic stability, permeability, and lipophilicity. Evidence is provided to suggest that the optimality criterion method may provide a better quantitative description of neighborhood behavior than the optimal diagonal method.