NMR-based methods and strategies for drug discovery

Nuclear Magnetic Resonance (NMR) spectroscopy has long been a favourite tool of chemists interested in host-guest systems because it permits access to a wealth of information about the molecular recognition reaction. NMR has evolved dramatically in the last 15 years and, in parallel with the development of NMR methods for the determination of protein structure, a variety of tools aimed at detecting protein ligand interactions have been proposed and are being now used both in industrial and academic laboratories as valuable tools for structure-based drug discovery. Very recent developments have considerably increased the fraction of therapeutic targets that can be tackled by NMR and significantly reduced the amount of sample required for analysis; in this tutorial review we outline the essential NMR-based techniques and describe some examples of their implementation as part of drug discovery programmes.     

RAMPED-UP NMR: multiplexed NMR-based screening for drug discovery

The crucial step in drug discovery is the identification of a lead compound from a vast chemical library by any number of screening techniques. NMR-based screening has the advantage of directly detecting binding of a compound to the target. The spectra resulting from these screens can also be very complex and difficult to analyze, making this an inefficient process. We present here a method, RAMPED-UP NMR, (Rapid Analysis and Multiplexing of Experimentally Discriminated Uniquely Labeled Proteins using NMR) which generates simple spectra which are easy to interpret and allows several proteins to be screened simultaneously. In this method, the proteins to be screened are uniquely labeled with one amino acid type. There are several benefits derived from this unique labeling strategy: the spectra are greatly simplified, resonances that are most likely to be affected by binding are the only ones observed, and peaks that yield little or no information upon binding are eliminated, allowing the analysis of multiple proteins easily and simultaneously. We demonstrate the ability of three different proteins to be analyzed simultaneously for binding to two different ligands. This method will have significant impact in the use of NMR spectroscopy for both the lead generation and lead optimization phases of drug discovery by its ability to increase screening throughput and the ability to examine selectivity. To the best of our knowledge, this is the first time in any format that multiple proteins can be screened in one tube.     

Applications of SHAPES screening in drug discovery

The SHAPES strategy combines nuclear magnetic resonance (NMR) screening of a library of small drug-like molecules with a variety of complementary methods, such as virtual screening, high throughput enzymatic assays, combinatorial chemistry, X-ray crystallography, and molecular modeling, in a directed search for new medicinal chemistry leads. In the past few years, the SHAPES strategy has found widespread utility in pharmaceutical research. To illustrate a variety of different implementations of the method, we will focus in this review on recent applications of the SHAPES strategy in several drug discovery programs at Vertex Pharmaceuticals.     

Novel strategy for three-dimensional fragment-based lead discovery

Fragment-based drug design (FBDD) is considered a promising approach in lead discovery. However, for a practical application of this approach, problems remain to be solved. Hence, a novel practical strategy for three-dimensional lead discovery is presented in this work. Diverse fragments with spatial positions and orientations retained in separately adjacent regions were generated by deconstructing well-aligned known inhibitors in the same target active site. These three-dimensional fragments retained their original binding modes in the process of new molecule construction by fragment linking and merging. Root-mean-square deviation (rmsd) values were used to evaluate the conformational changes of the component fragments in the final compounds and to identify the potential leads as the main criteria. Furthermore, the successful validation of our strategy is presented on the basis of two relevant tumor targets (CDK2 and c-Met), demonstrating the potential of our strategy to facilitate lead discovery against some drug targets.     

Modern drug discovery technologies: opportunities and challenges in lead discovery

The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets. Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced technologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges.     

Computer-aided drug design: lead discovery and optimization

Over the past decade, there have been remarkable advances in the area of computer-aided drug design (CADD), which has been applied at almost all stages in the drug discovery pipeline. The generation of initial lead compounds and the subsequent optimization aimed at improving potency and pharmacological properties are the core activities among all. The development in these aspects over the past years will be the focus of this review.     

Computational chemistry in drug lead discovery and design

The main contributions of our group during the last 15 years developing and using biomolecular simulation tools in drug lead discovery and design, in close collaboration with experimental researchers, are presented. Special emphasis has been given to methodological improvements in the following areas: (1) target homology modeling incorporating knowledge about known ligands to accurately characterize the binding site; (2) designing alternative strategies to account for protein flexibility in high-throughput docking; (3) development of stochastic- and normal-mode-based methods to de novo design structurally diverse protein conformers; (4) development and validation of quantum mechanical semi-empirical linear-scaling calculations to correctly estimate ligand binding free energy. Several successful cases of computer-aided drug discovery are also presented, especially our recent work on viral targets.     

PRO_SELECT: Combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology

This paper describes a novel methodology, PRO_SELECT, which combines elements of structure-based drug design and combinatorial chemistry to create a new paradigm for accelerated lead discovery. Starting with a synthetically accessible template positioned in the active site of the target of interest, PRO_SELECT employs database searching to generate lists of potential substituents for each substituent position on the template. These substituents are selected on the basis of their being able to couple to the template using known synthetic routes and their possession of the correct functionality to interact with specified residues in the active site. The lists of potential substituents are then screened computationally against the active site using rapid algorithms. An empirical scoring function, correlated to binding free energy, is used to rank the substituents at each position. The highest scoring substituents at each position can then be examined using a variety of techniques and a final selection is made. Combinatorial enumeration of the final lists generates a library of synthetically accessible molecules, which may then be prioritised for synthesis and assay. The results obtained using PRO_SELECT to design thrombin inhibitors are briefly discussed.     

ALOHA: a novel probability fusion approach for scoring multi-parameter drug-likeness during the lead optimization stage of drug discovery

Automated lead optimization helper application (ALOHA) is a novel fitness scoring approach for small molecule lead optimization. ALOHA employs a series of generalized Bayesian models trained from public and proprietary pharmacokinetic, absorption, distribution, metabolism, and excretion, and toxicology data to determine regions of chemical space that are likely to have excellent drug-like properties. The input to ALOHA is a list of molecules, and the output is a set of individual probabilities as well as an overall probability that each of the molecules will pass a panel of user selected assays. In addition to providing a summary of how and when to apply ALOHA, this paper will discuss the validation of ALOHA’s Bayesian models and probability fusion approach. Most notably, ALOHA is demonstrated to discriminate between members of the same chemical series with strong statistical significance, suggesting that ALOHA can be used effectively to select compound candidates for synthesis and progression at the lead optimization stage of drug discovery.     

SAR by ILOEs: an NMR-based approach to reverse chemical genetics

Reverse chemical genetics is an emerging technique that makes use of small molecule inhibitors to characterize how a protein functions. In this regard, we have developed an NMR-based approach (SAR by ILOEs) that enables the identification of high affinity ligands for a given protein target without the need of a specific assay. Our approach is of general applicability and could result very powerful in reverse chemical-genetics studies, target validation, and lead discovery. We report a recent application on the design and synthesis of compounds that inhibit protein-membrane interactions.     

A scalable approach to combinatorial library design for drug discovery

In this paper, we propose an algorithm for the design of lead generation libraries required in combinatorial drug discovery. This algorithm addresses simultaneously the two key criteria of diversity and representativeness of compounds in the resulting library and is computationally efficient when applied to a large class of lead generation design problems. At the same time, additional constraints on experimental resources are also incorporated in the framework presented in this paper. A computationally efficient scalable algorithm is developed, where the ability of the deterministic annealing algorithm to identify clusters is exploited to truncate computations over the entire data set to computations over individual clusters. An analysis of this algorithm quantifies the tradeoff between the error due to truncation and computational effort. Results applied on test data sets corroborate the analysis and show improvement by factors as large as 10 or more, depending on the data sets.     

HAD: an automated database tool for analyzing screening hits in drug discovery

Collecting, organizing, and reviewing chemical information associated with screening hits are human time-consuming. The task depends highly on the individual, and human errors may result in missing leads or wasting resources. To overcome these hurdles, we have developed a decision support system, Hits Analysis Database (HAD). HAD is a software tool that automatically generates an ISIS database file containing compound structures, biological activities, calculated properties such as clogP, hazard fragment labels, structure classifications, etc. All data are processed by available software and packed into a single SD file. In addition to search capabilities, HAD provides an overview of structural classes and associated activity statistics. Chemical structures can be organized by maximum common substructure clustering. The ease of use and customized features make HAD a chief tool in lead selection processes.     

In the pursuit for better actinide ligands: an efficient strategy for their discovery

A novel method for the efficient discovery of new types of minor actinide (MA) ligands is based on the unique combination of "tea bag" split pool combinatorial chemistry and screening based on the inherent radioactivity of the complexed cations. Four multicoordinating Am(3+) chelating groups, such as CMPO (diphenylcarbamoylmethyl)phosphine oxide), PICO (picolinamide), DGA (N,N'-dimethyldiglycoldiamide), and MPMA (N-methyl-N-phenylmalonamide), on a trityl platform immobilized on TentaGelS served as a model library for the development of the screening method. This model library was screened under various conditions (i.e., 0.001 M < or = [HNO3] < or = 3 M, NaNO3 < or = 4 M, and [Eu] < or = 10 x [ligand]) showing competitive extraction of the four ligands. Other libraries of 9 and 72 members were synthesized by functionalization of the trityl platform with ligating groups that are composed of four building blocks (including at least one amide and one (phosphoric) hydrazone moiety). The screening of these two libraries resulted in the discovery of two multicoordinate ligands that contain ligating groups previously not known to complex Am(3+). Both are N-isopropyl amides terminated with a p-methoxyphenyl hydrazide (A2B1C1D10 K(D(Am)) = 2197) or a p-nitrophenyl hydrazide (A2B1C1D11 K(D(Am)) =1989) moiety, respectively. They are more efficient than the immobilized tritylCMPO ligand (K(D(Am)) = 1280) at 3 M HNO3. This method has the advantages of a high analytical sensitivity and the direct comparison of the extraction results. The method also allows the competitive screening of multiple nuclides which can be quantified by their radioactive emission spectrum.     

A novel approach in drug discovery: synthesis of estrone--talaromycin natural product hybrids

Hetero-Diels-Alder reaction of the steroidal exocyclic enol ethers 14 and 15, obtained from the secoestrones 8 and 9 by reduction, iodoetherification, and elimination, with ethyl O-benzoyldiformylacetate (16) leads to the spiroacetals 17 and 18 as a mixture of four diastereomers. Reduction of the major diastereomers 17a and 18a with DIBAH and subsequent hydrogenation yields the novel natural product hybrids 21, 23, 24, and 25, which possess the structural features of the steroid estrone (7) and the mycotoxin talaromycin 6.     

Circular dichroism in drug discovery and development: an abridged review

Chirality plays a fundamental role in determining the pharmacodynamic and pharmacokinetic properties of drugs, and contributes significantly to our understanding of the mechanisms that lie behind biorecognition phenomena. Circular dichroism spectroscopy is the technique of choice for determining the stereochemistry of chiral drugs and proteins, and for monitoring and characterizing molecular recognition phenomena in solution. The role of chirality in our understanding of recognition phenomena at the molecular level is discussed here via several selected systems of interest in the drug discovery and development area. The examples were selected in order to underline the utility of circular dichroism in emerging studies of protein–protein interactions in biological context. In particular, the following aspects are discussed here: the relationship between stereochemistry and pharmacological activity—stereochemical characterization of new leads and drugs; stereoselective binding of leads and drugs to target proteins—the binding of drugs to serum albumins; conformational transitions of peptides and proteins of physiological relevance, and the stereochemical characterization of therapeutic peptides.     

A method for automatic generation of novel chemical structures and its potential applications to drug discovery.

A novel method for generation of chemical structures of potential pharmaceutical interest is presented. Structures are generated by random combination of known fragments and selected by statistical topological techniques. The power of the method lies in the great profusion of candidates generated together with the extremely high selectivity imposed by the techniques of selection.     

The beautiful cell: high-content screening in drug discovery

The term “high-content screening” has become synonymous with imaging screens using automated microscopes and automated image analysis. The term was coined a little over 10 years ago. Since then the technology has evolved considerably and has established itself firmly in the drug discovery and development industry. Both the instruments and the software controlling the instruments and analyzing the data have come to maturity, so the full benefits of high-content screening can now be realized. Those benefits are the capability of carrying out phenotypic multiparametric cellular assays in an unbiased, fully automated, and quantitative fashion. Automated microscopes and automated image analysis are being applied at all stages of the drug discovery and development pipeline. All major pharmaceutical companies have adopted the technology and it is in the process of being embraced broadly by the academic community. This review aims at describing the current capabilities and limits of the technology as well as highlighting necessary developments that are required to exploit fully the potential of high-content screening and analysis.