Douglas B. Kitchen

Bio: Douglas B. Kitchen is an academic researcher from Albany Molecular Research, Inc.. The author has contributed to research in topics: Virtual screening & Retinol binding protein 4. The author has an hindex of 15, co-authored 25 publications receiving 2922 citations.

Docking and scoring in virtual screening for drug discovery: methods and applications.

Abstract: Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches

Computational models to predict blood-brain barrier permeation and CNS activity.

Abstract: The blood–brain permeation of a structurally diverse set of 281 compounds was modeled using linear regression and a multivariate genetic partial least squares (G/PLS) approach. Key structural features affecting the logarithm of blood–brain partitioning (logBB) were captured through statistically significant quantitative structure–activity relationship (QSAR) models. These relationships reveal the importance of logP, polar surface area, and a variety of electrotopological indices for accurate predictions of logBB. The best models reveal an excellent correlation (r > 0.9) for a training set of 58 compounds. Likewise, the comparison of the average logBB values obtained from an ensemble of QSAR models with experimental values also verifies the statistical quality of the models (r > 0.9). The models provide good agreement (r ∼ 0.7) between the predicted logBB values for 34 molecules in the external validation set and the experimental values. To further validate the models for use during the drug discovery process, a prediction set of 181 drugs with reported CNS penetration data was used. A >70% success rate is obtained by using any of the QSAR models in the qualitative prediction for CNS permeable (active) drugs. A lower success rate (∼60%) was obtained for the best model for CNS impermeable (inactive) drugs. Combining the predictions obtained from all the models (consensus) did not significantly improve the discrimination of CNS active and CNS inactive molecules. Finally, using the therapeutic classification as a guiding tool, the CNS penetration capability of over 2000 compounds in the Synthline® database was estimated. The results were very similar to the smaller set of 181 compounds.

Design, synthesis, and evaluation of nonretinoid retinol binding protein 4 antagonists for the potential treatment of atrophic age-related macular degeneration and Stargardt disease.

Abstract: Accumulation of lipofuscin in the retina is associated with pathogenesis of atrophic age-related macular degeneration and Stargardt disease. Lipofuscin bisretinoids (exemplified by N-retinylidene-N-retinylethanolamine) seem to mediate lipofuscin toxicity. Synthesis of lipofuscin bisretinoids depends on the influx of retinol from serum to the retina. Compounds antagonizing the retinol-dependent interaction of retinol-binding protein 4 (RBP4) with transthyretin in the serum would reduce serum RBP4 and retinol and inhibit bisretinoid formation. We recently showed that A1120 (3), a potent carboxylic acid based RBP4 antagonist, can significantly reduce lipofuscin bisretinoid formation in the retinas of Abca4–/– mice. As part of the NIH Blueprint Neurotherapeutics Network project we undertook the in vitro exploration to identify novel conformationally flexible and constrained RBP4 antagonists with improved potency and metabolic stability. We also demonstrate that upon acute and chronic dosing in rats, 43, a pot...

Design and Characterization of Novel Covalent Bromodomain and Extra-Terminal Domain (BET) Inhibitors Targeting a Methionine.

Abstract: BET proteins are key epigenetic regulators that regulate transcription through binding to acetylated lysine (AcLys) residues of histones and transcription factors through bromodomains (BDs). The disruption of this interaction with small molecule bromodomain inhibitors is a promising approach to treat various diseases including cancer, autoimmune and cardiovascular diseases. Covalent inhibitors can potentially offer a more durable target inhibition leading to improved in vivo pharmacology. Here we describe the design of covalent inhibitors of BRD4(BD1) that target a methionine in the binding pocket by attaching an epoxide warhead to a suitably oriented noncovalent inhibitor. Using thermal denaturation, MALDI-TOF mass spectrometry, and an X-ray crystal structure, we demonstrate that these inhibitors selectively form a covalent bond with Met149 in BRD4(BD1) but not other bromodomains and provide durable transcriptional and antiproliferative activity in cell based assays. Covalent targeting of methionine offe...

AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

Abstract: We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique.

Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments

Abstract: Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.

Drug repurposing: progress, challenges and recommendations

Abstract: Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.

A semiempirical free energy force field with charge-based desolvation.

Abstract: The authors describe the development and testing of a semiempirical free energy force field for use in AutoDock4 and similar grid-based docking methods. The force field is based on a comprehensive thermodynamic model that allows incorporation of intramolecular energies into the predicted free energy of binding. It also incorporates a charge-based method for evaluation of desolvation designed to use a typical set of atom types. The method has been calibrated on a set of 188 diverse protein-ligand complexes of known structure and binding energy, and tested on a set of 100 complexes of ligands with retroviral proteases. The force field shows improvement in redocking simulations over the previous AutoDock3 force field.

Molecular docking: a powerful approach for structure-based drug discovery.

Abstract: Molecular docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available molecular docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarized. The differences in and performance of available docking software are also discussed. Flexible receptor molecular docking approaches, especially those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential solution to flexible receptor docking problems. Three application examples of molecular docking approaches for drug discovery are provided.