Chris L. Waller

Progress in predicting human ADME parameters in silico.

Understanding the development of a scientific approach is a valuable exercise in gauging the potential directions the process could take in the future. The relatively short history of applying computational methods to absorption, distribution, metabolism and excretion (ADME) can be split into defined periods. The first began in the 1960s and continued through the 1970s with the work of Corwin Hansch et al. Their models utilized small sets of in vivo ADME data. The second era from the 1980s through 1990s witnessed the widespread incorporation of in vitro approaches as surrogates of in vivo ADME studies. These approaches fostered the initiation and increase in interpretable computational ADME models available in the literature. The third era is the present were there are many literature data sets derived from in vitro data for absorption, drug-drug interactions (DDI), drug transporters and efflux pumps [P-glycoprotein (P-gp), MRP], intrinsic clearance and brain penetration, which can theoretically be used to predict the situation in vivo in humans. Combinatorial synthesis, high throughput screening and computational approaches have emerged as a result of continual pressure on pharmaceutical companies to accelerate drug discovery while decreasing drug development costs. The goal has become to reduce the drop-out rate of drug candidates in the latter, most expensive stages of drug development. This is accomplished by increasing the failure rate of candidate compounds in the preclinical stages and increasing the speed of nomination of likely clinical candidates. The industry now understands the reasons for clinical failure other than efficacy are mainly related to pharmacokinetics and toxicity. The late 1990s saw significant company investment in ADME and drug safety departments to assess properties such as metabolic stability, cytochrome P-450 inhibition, absorption and genotoxicity earlier in the drug discovery paradigm. The next logical step in this process is the evaluation of higher throughput data to determine if computational (in silico) models can be constructed and validated from it. Such models would allow an exponential increase in the number of compounds screened virtually for ADME parameters. A number of researchers have started to utilize in silico, in vitro and in vivo approaches in parallel to address intestinal permeability and cytochrome P-450-mediated DDI. This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress.

A crowdsourcing evaluation of the NIH chemical probes.

Between 2004 and 2008, the US National Institutes of Health Molecular Libraries and Imaging initiative pilot phase funded 10 high-throughput screening centers, resulting in the deposition of 691 assays into PubChem and the nomination of 64 chemical probes. We crowdsourced the Molecular Libraries and Imaging initiative output to 11 experts, who expressed medium or high levels of confidence in 48 of these 64 probes.

Molecular determinants of hormone mimicry: Halogenated aromatic hydrocarbon environmental agents

The potential of ostensibly structurally diverse environmental chemicals to modulate endocrine processes in biological systems has been recognized. Difficulty in classifying endocrine system modulators by chemical structure may in large part be due to lack of understanding of mechanisms of action. New developments in understanding nuclear receptor mechanisms of hormone action support a more complex mechanism, possibly involving dimerization/aggregation events leading to multimeric receptor complexes in agonist action. Because of the requirement for high structural specificity in agonist action, it is suggested that most environmental chemicals of concern are likely to function as imperfect hormones with partial agonist-antagonist properties, especially at environmentally realistic concentrations. In the absence of having appropriately placed molecular recognition domains to affect agonist action, partial agonism-antagonism may be associated with favorable low-energy conformational flexibility and complementary receptor protein flexibility. The halogenated aromatic hydrocarbons are of particular concern as hormone mimics since they often have (1) similar molecular recognition factors but in many cases relatively more flexible structures, (2) similar bulk physico-chemical properties controlling uptake and distribution in biological systems, and (3) are relatively more resistant to metabolism and elimination. Some important molecular reactivity properties underlying thyromimetic and estrogenic actions of some of these chemicals are identified and described in terms of structure-activity relationships (SARs). It is proposed that specificity of hormone action in the nucleus could be associated with differential interaction of ligand-bound receptor dimeric forms with other transcription factors specific to the target cell. The small-molecule ligand can be viewed as playing a central, multifunctional role in nuclear receptor action as an organic unmasking and reclustering agent for critical macromolecules. Evidence is discussed in support of a nuclear heterodimerization model for dioxin and related compound action involving a structural transition mechanism. These models with some molecular detail also have utility in understanding the different structural properties of agonists and antagonists. There would appear to be ample opportunities for environmental chemicals to act as antagonists for multiple receptor systems with little more than anchor-ring similarities in structure. The application of three-dimensional quantitative structure-activity (3D QSAR) models incorporating such structural information should be a useful adjunct for identifying endocrine system modulating chemicals. This data has implications for (1) improved drug design, (2) understanding of chemical interaction toxicity, (3) removing undesirable chemicals from our environment, and (4) reducing their chemical release.

Using three-dimensional quantitative structure-activity relationships to examine estrogen receptor binding affinities of polychlorinated hydroxybiphenyls

Certain phenyl-substituted hydrocarbons of environmental concern have the potential to disrupt the endocrine system of animals, apparently in association with their estrogenic properties. Competition with natural estrogens for the estrogen receptor is a possible mechanism by which such effects could occur. We used comparative molecular field analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (QSAR) paradigm, to examine the underlying structural properties of ortho-chlorinated hydroxybiphenyl analogs known to bind to the estrogen receptor. The cross-validated and conventional statistical results indicate a high degree of internal predictability for the molecules included in the training data set. In addition to the phenolic (A) ring system, conformational restriction of the overall structure appears to play an important role in estrogen receptor binding affinity. Hydrophobic character as assessed using hydropathic interaction fields also contributes in a positive way to binding affinity. The CoMFA-derived QSARs may be useful in examining the estrogenic activity of a wider range of phenyl-substituted hydrocarbons of environmental concern. ImagesFigure 1.Figure 2.

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

A comparative QSAR study using CoMFA, HQSAR, and FRED/SKEYS paradigms for estrogen receptor binding affinities of structurally diverse compounds.

The three-dimensional quantitative structure-activity relationship (QSAR) technique of comparative molecular field analysis (CoMFA) has demonstrated the ability to provide accurate predictions for diverse chemical compounds when trained with molecules of diverse chemical type. Although predictive, the derivation and utilization of models of this type are quite computationally and person power intensive. It is this intensity that pragmatically limits the widespread implementation of these models as predictive tools. In this study, two newer QSAR techniques were evaluated as possible alternatives to CoMFA based QSAR models for the purpose of rapidly identifying estrogen receptor ligands from diverse collections of molecules. The first of these is Hologram QSAR, or HQSAR. HQSAR utilizes Tripos molecular fingerprints as descriptors in conjunction with partial least squares (PLS) regression and cross-validation routines. The HQSAR technique demonstrated the ability to rapidly develop QSAR models independent of the intense user input (i.e. geometry optimization, conformational analysis, and molecular superposition were not required). Second, a newly developed QSAR paradigm that utilizes Molecular Design Limited (MDL) substructure keys (SKEYS) as descriptors in combination with an evolutionary algorithm, Fast Random Elimination of Descriptors (FRED), was evaluated. By utilizing the FRED/SKEYS algorithm, a simple substructure-based QSAR model was derived that was comparable in statistical robustness and predictive ability to both CoMFA and HQSAR derived models. A comparison of the utility of these three approaches as computational tools for the rapid identification of estrogen receptor ligands as potential endocrine disruptors as assessed by model predictive ability will be described.

Four disruptive strategies for removing drug discovery bottlenecks

Drug discovery is shifting focus from industry to outside partners and, in the process, creating new bottlenecks. Technologies like high throughput screening (HTS) have moved to a larger number of academic and institutional laboratories in the USA, with little coordination or consideration of the outputs and creating a translational gap. Although there have been collaborative public-private partnerships in Europe to share pharmaceutical data, the USA has seemingly lagged behind and this may hold it back. Sharing precompetitive data and models may accelerate discovery across the board, while finding the best collaborators, mining social media and mobile approaches to open drug discovery should be evaluated in our efforts to remove drug discovery bottlenecks. We describe four strategies to rectify the current unsustainable situation.

Chemical space: missing pieces in cheminformatics.

ABSTRACTCheminformatics is at a turning point, the pharmaceutical industry benefits from using the various methods developed over the last twenty years, but in our opinion we need to see greater development of novel approaches that non-experts can use. This will be achieved by more collaborations between software companies, academics and the evolving pharmaceutical industry. We suggest that cheminformatics should also be looking to other industries that use high performance computing technologies for inspiration. We describe the needs and opportunities which may benefit from the development of open cheminformatics technologies, mobile computing, the movement of software to the cloud and precompetitive initiatives.

Prediction of Drug-Like Molecular Properties

Preventing drug-drug interactions and reducing drug-related mortalities dictate cleaner and costlier medicines. The cost to bring a new drug to market has increased dramatically over the last 10 years, with post-discovery activities (preclinical and clinical) costs representing the majority of the spend. With the ever-increasing scrutiny that new drug candidates undergo in the post-discovery assessment phases, there is increasing pressure on discovery to deliver higher-quality drug candidates. Given that compound attrition in the early clinical stages can often be attributed to metabolic liabilities, it has been of great interest lately to implement predictive measures of metabolic stability/ liability in the drug design stage of discovery. The solution to this issue is wrapped in understanding the basic of the cytochrome P450 (CYP) enzymes functions and structures. Recently, experimental information on the structure of a variety of cytochrome P450 enzymes, major contributors to phase I metabolism, has become readily available. This, coupled with the availability of experimental information on substrate specificities, has lead to the development of numerous computational models (macromolecular, pharmacophore, and structure-activity) for the rationalization and prediction of CYP liabilities. A comprehensive review of these models is presented in this chapter.

PGVL Hub: An Integrated Desktop Tool for Medicinal Chemists to Streamline Design and Synthesis of Chemical Libraries and Singleton Compounds

PGVL Hub is an integrated molecular design desktop tool that has been developed and globally deployed throughout Pfizer discovery research units to streamline the design and synthesis of combinatorial libraries and singleton compounds. This tool supports various workflows for design of singletons, combinatorial libraries, and Markush exemplification. It also leverages the proprietary PGVL virtual space (which contains 10(14) molecules spanned by experimentally derived synthesis protocols and suitable reactants) for lead idea generation, lead hopping, and library design. There had been an intense focus on ease of use, good performance and robustness, and synergy with existing desktop tools such as ISIS/Draw and SpotFire. In this chapter we describe the three-tier enterprise software architecture, key data structures that enable a wide variety of design scenarios and workflows, major technical challenges encountered and solved, and lessons learned during its development and deployment throughout its production cycles. In addition, PGVL Hub represents an extendable and enabling platform to support future innovations in library and singleton compound design while being a proven channel to deliver those innovations to medicinal chemists on a global scale.