Two ‘Golden Ratio’ indices in fragment-based drug discovery

Benzylic C-H isocyanation/amine coupling sequence enabling high-throughput synthesis of pharmaceutically relevant ureas

C(sp³)–H functionalization methods provide an ideal synthetic platform for medicinal chemistry; however, such methods are often constrained by practical limitations. The present study outlines a C(sp³)–H isocyanation protocol that enables the synthesis of diverse, pharmaceutically relevant benzylic ureas in high-throughput format. The operationally simple C–H isocyanation method shows high site selectivity and good functional group tolerance, and uses commercially available catalyst components and reagents [CuOAc, 2,2′-bis(oxazoline) ligand, (trimethylsilyl)isocyanate, and N-fluorobenzenesulfonimide]. The isocyanate products may be used without isolation or purification in a subsequent coupling step with primary and secondary amines to afford hundreds of diverse ureas. These results provide a template for implementation of C–H functionalization/cross-coupling in drug discovery.

A copper-based catalyst system composed of commercially available reagents enables C–H isocyanation with exquisite (hetero)benzylic site selectivity, enabling high-throughput access to pharmaceutically relevant ureas via coupling with amines.

Fragment-based drug design of nature-inspired compounds

Natural product (NP)-derived drugs can be extracts, biological macromolecules, or purified small molecule substances. Small molecule drugs can be originally purified from NPs, can represent semisynthetic molecules, natural fragments containing small molecules, or are fully synthetic molecules that mimic natural compounds. New semisynthetic NP-like drugs are entering the pharmaceutical market almost every year and reveal growing interests in the application of fragment-based approaches for NPs. Thus, several NP databases were constructed to be implemented in the fragment-based drug design (FBDD) workflows. FBDD has been established previously as an approach for hit identification and lead generation. Several biophysical and computational methods are used for fragment screening to identify potential hits. Once the fragments within the binding pocket of the protein are identified, they can be grown, linked, or merged to design more active compounds. This work discusses applications of NPs and NP scaffolds to FBDD. Moreover, it briefly reviews NP databases containing fragments and reports on case studies where the approach has been successfully applied for the design of antimalarial and anticancer drug candidates.

Exploring the Chemical Space of Cytochrome P450 Inhibitors Using Integrated Physicochemical Parameters, Drug Efficiency Metrics and Decision Tree Models

The cytochrome P450s (CYPs) play a central role in the metabolism of various endogenous and exogenous compounds including drugs. CYPs are vulnerable to inhibition and induction which can lead to adverse drug reactions. Therefore, insights into the underlying mechanism of CYP450 inhibition and the estimation of overall CYP inhibitor properties might serve as valuable tools during the early phases of drug discovery. Herein, we present a large data set of inhibitors against five major metabolic CYPs (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) for the evaluation of important physicochemical properties and ligand efficiency metrics to define property trends across various activity levels (active, efficient and inactive). Decision tree models for CYP inhibition were developed with an accuracy >90% for both the training set and 10-folds cross validation. Overall, molecular weight (MW), hydrogen bond acceptors/donors (HBA/HBD) and lipophilicity (clogP/logPo/w) represent important physicochemical descriptors for CYP450 inhibitors. However, highly efficient CYP inhibitors show mean MW, HBA, HBD and logP values between 294.18–482.40,5.0–8.2,1–7.29 and 1.68–2.57, respectively. Our results might help in optimization of toxicological profiles associated with new chemical entities (NCEs), through a better understanding of inhibitor properties leading to CYP-mediated interactions.

LeadOp+R: Structure-Based Lead Optimization With Synthetic Accessibility

We previously described a structure-based fragment hopping for lead optimization using a pre-docked fragment database, “LeadOp,” that conceptually replaced “bad” fragments of a ligand with “good” fragments while leaving the core of the ligand intact thus improving the compound's activity. LeadOp was proven to optimize the query molecules and systematically developed improved analogs for each of our example systems. However, even with the fragment-based design from common building blocks, it is still a challenge for synthesis. In this work, “LeadOp+R” was developed based on 198 classical chemical reactions to consider the synthetic accessibility while optimizing leads. LeadOp+R first allows user to identify a preserved space defined by the volume occupied by a fragment of the query molecule to be preserved. Then LeadOp+R searches for building blocks with the same preserved space as initial reactants and grows molecules toward the preferred receptor-ligand interactions according to reaction rules from reaction database in LeadOp+R. Multiple conformers of each intermediate product were considered and evaluated at each step. The conformer with the best group efficiency score would be selected as the initial conformer of the next building block until the program finished optimization for all selected receptor-ligand interactions. The LeadOp+R method was tested with two biomolecular systems: Tie-2 kinase and human 5-lipoxygenase. The LeadOp+R methodology was able to optimize the query molecules and systematically developed improved analogs for each of our example systems. The suggested synthetic routes for compounds proposed by LeadOp+R were the same as the published synthetic routes devised by the synthetic/organic chemists.

Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective

INTRODUCTION

Ligand efficiency metrics are almost universally accepted as a valuable indicator of compound quality and an aid to reduce attrition. Areas covered: In this review, the authors describe ligand efficiency metrics giving a balanced overview on their merits and points of weakness in order to enable the readers to gain an informed opinion. Relevant theoretical breakthroughs and drug-like properties are also illustrated. Several recent exemplary case studies are discussed in order to illustrate the main fields of application of ligand efficiency metrics. Expert opinion: As a medicinal chemist guide, ligand efficiency metrics perform in a context- and chemotype-dependent manner; thus, they should not be used as a magic box. Since the 'big bang' of efficiency metrics occurred more or less ten years ago and the average time to develop a new drug is over the same period, the next few years will give a clearer outlook on the increased rate of success, if any, gained by means of these new intriguing tools.

Fragment-Based Design, Synthesis, and Biological Evaluation of 1-Substituted-indole-2-carboxylic Acids as Selective Mcl-1 Inhibitors

Based on a known selective Mcl-1 inhibitor, 6-chloro-3-(3-(4-chloro-3,5-dimethylphenoxy)propyl)-1H-indole-2-carboxylic acid, we applied a fragment-based approach to obtain new molecules that extended into the p1 pocket of the BH3 groove and then exhibited binding selectivity for the Mcl-1 over the Bcl-2 protein. After we deconstructed the 1H-indole-2-carboxylic acid from the parental molecule, a benzenesulfonyl was substituted at the 1-position to adopt a geometry preferred for accessing the p1 pocket according to the binding mode of the parental molecule identified by X-ray crystallography. A linear relationship between the free energy of ligand binding (ΔG) and the count of non-hydrogen heavy atoms (HAC) was maintained during the molecular growing to occupy the p1 pocket. Finally, we not only obtained compound 12 with a 7.5-fold selectivity to Mcl-1 (K_i  = 0.48 µM by fluorescence polarization) over Bcl-2 (K_i  = 3.6 µM), but also provided evidence that additional occupation of the p1 pocket is more favorable for Mcl-1 than for Bcl-2 binding, and contributes more to Mcl-1 inhibition than occupation of the p2 pocket. Compound 12 exhibited a selective killing ability on Mcl-1-dependent cancer cells.

Compound high-quality criteria: a new vision to guide the development of drugs, current situation

For several decades, the pharmaceutical industry has suffered due to major issues such as reductions of the number of FDA approved drugs and biologics. Several analyses have been highlighted that the 'druglikeness' is one of the strategies to improve succeed rates of screening such as, for instance, high-throughput screening (HTS), and then hits (as starting point), leads and clinical candidates. It is clear that the improvement of compound quality accelerates the drug discovery projects. The monitoring of several indices to avoid 'molecular obesity' (ADMET problems) of final drugs from good-quality 'low-fat' starting points represents today a powerful strategy of optimization process. The development of the new guides to find drugs highlighting attempts at improving the attrition rate from hits to final medicines by focusing on how to improve the druggability of hits, leads and drugs during the drug discovery process represents a key approach to design next better generation of medicines.

Identification of ligand efficient, fragment-like hits from an HTS library: structure-based virtual screening and docking investigations of 2H- and 3H-pyrazolo tautomers for Aurora kinase A selectivity

Furanopyrimidine 1 (IC50 = 273 nM, LE = 0.36, LELP = 10.28) was recently identified by high-throughput screening (HTS) of an in-house library (125,000 compounds) as an Aurora kinase inhibitor. Structure-based hit optimization resulted in lead molecules with in vivo efficacy in a mouse tumour xenograft model, but no oral bioavailability. This is attributed to "molecular obesity", a common problem during hit to lead evolution during which degradation of important molecular properties such as molecular weight (MW) and lipophilicity occurs. This could be effectively tackled by the right choice of hit compounds for optimization. In this regard, ligand efficiency (LE) and ligand efficiency dependent lipophilicity (LELP) indices are more often used to choose fragment-like hits for optimization. To identify hits with appropriate LE, we used a MW cut-off <250, and pyrazole structure to filter HTS library. Next, structure-based virtual screening using software (Libdock and Glide) in the Aurora A crystal structure (PDB ID: 3E5A) was carried out, and the top scoring 18 compounds tested for Aurora A enzyme inhibition. This resulted in the identification of a novel tetrahydro-pyrazolo-isoquinoline hit 7 (IC50 = 852 nM, LE = 0.44, LELP = 8.36) with fragment-like properties suitable for further hit optimization. Moreover, hit 7 was found to be selective for Aurora A (Aurora B IC50 = 35,150 nM) and the possible reasons for selectivity investigated by docking two tautomeric forms (2H- and 3H-pyrazole) of 7 in Auroras A and B (PDB ID: 4AF3) crystal structures. This docking study shows that the major 3H-pyrazole tautomer of 7 binds in Aurora A stronger than in Aurora B.

Virtual screening of traditional Chinese medicine (TCM) database: identification of fragment-like lead molecules for filariasis target asparaginyl-tRNA synthetase

Lymphatic filariasis (LF) is a vector borne infectious disease caused by the nematode Wuchereria bancrofti, Brugia malayi, and Brugia timori. Over 120 million people are affected by LF in the world, of which two-thirds are in Asia. The infection restricts the normal flow of lymph from the infected area resulting in swelling of the extremities and causing permanent disability. As the available drugs for the treatment of LF are becoming ineffective due to the development of resistance, there is an urgent need to find new leads for drug development. In this study, asparaginyl-tRNA synthetase (AsnRS; PDB ID: 2XGT) essential for the protein bio-synthesis in the filarial nematode was used to carry out virtual screening (VS) of plant constituents from traditional Chinese medicine (TCM) database. Docking as well as E-pharmacophore based VS were carried out to identify the hits. The top scoring hits, Agri 1 (1,3,8-trihydroxy-4,5-dimethoxyxanthen-9-one-3-O-beta-D-glucopyranoside) and Agri 2 (5,7-dihydroxy-2-propylchromone 7-O-beta-D-glucopyranoside), constituents of Agrimonia pilosa, were selected for molecular dynamics (MD) simulation study for 10 ns. MD simulation showed that both the glycosides Agri 1 and Agri 2 were forming stable interactions with the target protein. Moreover, docking and MD simulation of the lead A (1,3,8-trihydroxy-4,5-dimethoxyxanthen-9-one; Mol. Wt.: 304.25; CLogP: 3.07) and lead B (5,7-dihydroxy-2-propylchromone; Mol. Wt.: 220.22; CLogP: 3.02), the aglycones of Agri 1 and Agri 2, respectively, were carried out with the target AsnRS. The in silico investigations of the aglycones suggest that the lead B could be a suitable fragment-like lead molecule for anti-filarial drug discovery.

High-throughput screening of effective siRNAs using luciferase-linked chimeric mRNA

The use of siRNAs to knock down gene expression can potentially be an approach to treat various diseases. To avoid siRNA toxicity the less transcriptionally active H1 pol III promoter, rather than the U6 promoter, was proposed for siRNA expression. To identify highly efficacious siRNA sequences, extensive screening is required, since current computer programs may not render ideal results. Here, we used CCR5 gene silencing as a model to investigate a rapid and efficient screening approach. We constructed a chimeric luciferase-CCR5 gene for high-throughput screening of siRNA libraries. After screening approximately 900 shRNA clones, 12 siRNA sequences were identified. Sequence analysis demonstrated that most (11 of the 12 sequences) of these siRNAs did not match those identified by available siRNA prediction algorithms. Significant inhibition of CCR5 in a T-lymphocyte cell line and primary T cells by these identified siRNAs was confirmed using the siRNA lentiviral vectors to infect these cells. The inhibition of CCR5 expression significantly protected cells from R5 HIV-1JRCSF infection. These results indicated that the high-throughput screening method allows efficient identification of siRNA sequences to inhibit the target genes at low levels of expression.

Setting expectations in molecular optimizations: Strengths and limitations of commonly used composite parameters

Over the past 15years there have been extensive efforts to understand and reduce the high attrition rates of drug candidates with an increased focus on physicochemical properties. The fruits of this labor have been the generation of numerous efficiency indices, metric-based rules and visualization tools to help guide medicinal chemists in the design of new compounds with more favorable properties. This deluge of information may have had the unintended consequence of further obfuscating molecular optimizations by the inability of these scoring functions, rules and guides to reach a consensus on when a particular transformation is identified as beneficial. In this manuscript, several composite parameters, or efficiency indices, are examined utilizing theoretical and experimental matched molecular pair analyses in order to understand the basis for how each will perform under varying scenarios of molecular optimizations. In contrast to empirically derived composite parameters based on heavy atom count, lipophilic efficiency (LipE) sets consistent expectations regardless of molecular weight or relative potency and can be used to generate consistent expectations for any matched molecular pair.

Finding the sweet spot: the role of nature and nurture in medicinal chemistry

Given its position at the heart of small-molecule drug discovery, medicinal chemistry has an important role in tackling the well-known productivity challenges in pharmaceutical research and development. In recent years, extensive analyses of successful and failed discovery compounds and drug candidates have improved our understanding of the role of physicochemical properties in drug attrition. Based on the clarified challenges in finding the 'sweet spot' in medicinal chemistry programmes, we suggest that this goal can be achieved through a combination of first identifying chemical starting points with appropriate 'nature' and then rigorously 'nurturing' them during lead optimization. Here, we discuss scientific, strategic, organizational and cultural considerations for medicinal chemistry practices, with the aim of promoting more effective use of what is already known, as well as a wider appreciation of the risks of pursuing suboptimal compounds.

Emerging role of surface plasmon resonance in fragment-based drug discovery

Surface plasmon resonance (SPR) offers a method of biophysical fragment screening that is fast, efficient, cost effective and accurate. SPR is increasingly being adopted as a secondary assay to validate fragment hits. Recently, technical advances have resulted in the emergence of SPR as a primary screening methodology for fragment-based drug discovery. Moreover, SPR biosensor assays can be developed for a wide range of proteins, including membrane proteins, such as G-protein-coupled receptors. In this review, we discuss the advantages and limitations of SPR fragment screening including experimental consideration of reducing false positive and false negative rates to a minimum. We discuss how ligand efficiency can be used both as a method to eliminate false positives and to understand which fragments in a library may be a source of false negatives.

Screening for GPCR Ligands Using Surface Plasmon Resonance

G-protein coupled receptors (GPCRs) are a class of drug targets of primary importance. However, receptor assays are based on measurement of either ligand displacement or downstream functional responses, rather than direct observation of ligand binding. Issues of allosteric modulation, probe dependence, and functional selectivity create challenges in selecting suitable assays formats. Therefore, a method that directly measures GPCR–ligand interactions, independent of binding site, probe, and signaling pathway would be a useful primary and orthogonal screening method. We have developed a GPCR biosensor assay protocol that offers the opportunity for high-throughput label-free screening that directly measures GPCR–ligand interactions. The biosensor-based direct screening method identifies the interaction of both orthosteric and allosteric ligands with solubilized, native GPCRs, in a label-free and cell-free environment, thus overcoming the limitations of indirect and displacement assay methods. We exemplify the method by the discovery of novel ligands for the chemokine receptor, CCR5, that are ligand efficient fragments.

Designing a diverse high-quality library for crystallography-based FBDD screening

A well-chosen set of fragments is able to cover a large chemical space using a small number of compounds. The actual size and makeup of the fragment set is dependent on the screening method since each technique has its own practical limits in terms of the number of compounds that can be screened and requirements for compound solubility. In this chapter, an overview of the general requirements for a fragment library is presented for different screening platforms. In the case of the FBDD work at Johnson & Johnson Pharmaceutical Research and Development, L.L.C., our main screening technology is X-ray crystallography. Since every soaked protein crystal needs to be diffracted and a protein structure determined to delineate if a fragment binds, the size of our initial screening library cannot be a rate-limiting factor. For this reason, we have chosen 900 as the appropriate primary fragment library size. To choose the best set, we have developed our own mix of simple property ("Rule of 3") and "bad" substructure filtering. While this gets one a long way in terms of limiting the fragment pool, there are still tens of thousands of compounds to choose from after this initial step. Many of the choices left at this stage are not drug-like, so we have developed an FBDD Score to help select a 900-compound set. The details of this score and the filtering are presented.

Prediction of Positions of Active Compounds Makes It Possible To Increase Activity in Fragment-Based Drug Development

We have developed a computational method that predicts the positions of active compounds, making it possible to increase activity as a fragment evolution strategy. We refer to the positions of these compounds as the active position. When an active fragment compound is found, the following lead generation process is performed, primarily to increase activity. In the current method, to predict the location of the active position, hydrogen atoms are replaced by small side chains, generating virtual compounds. These virtual compounds are docked to a target protein, and the docking scores (affinities) are examined. The hydrogen atom that gives the virtual compound with good affinity should correspond to the active position and it should be replaced to generate a lead compound. This method was found to work well, with the prediction of the active position being 2 times more efficient than random synthesis. In the current study, 15 examples of lead generation were examined. The probability of finding active positions among all hydrogen atoms was 26%, and the current method accurately predicted 60% of the active positions.

Prediction of ligand-binding sites of proteins by molecular docking calculation for a random ligand library

A new approach to predicting the ligand-binding sites of proteins was developed, using protein-ligand docking computation. In this method, many compounds in a random library are docked onto the whole protein surface. We assumed that the true ligand-binding site would exhibit stronger affinity to the compounds in the random library than the other sites, even if the random library did not include the ligand corresponding to the true binding site. We also assumed that the affinity of the true ligand-binding site would be correlated to the docking scores of the compounds in the random library, if the ligand-binding site was correctly predicted. We call this method the molecular-docking binding-site finding (MolSite) method. The MolSite method was applied to 89 known protein-ligand complex structures extracted from the Protein Data Bank, and it predicted the correct binding sites with about 80-99% accuracy, when only the single top-ranked site was adopted. In addition, the average docking score was weakly correlated to the experimental protein-ligand binding free energy, with a correlation coefficient of 0.44.

Fangjiomics: in search of effective and safe combination therapies

Millennia-old Chinese medicine treats disease with many combination therapies involving ingredients used in clinic practice. Fangjiomics is the science of identifying and designing effective mixtures of bioactive agents and elucidating their modes of action beyond those of Chinese patent medicines. Omics profiling and quantitative optimal modeling have been used to associate the various responses with biological pathways related to disease phenotype. Fangjiomics seeks to study myriad compatible combinations that may act through multiple targets, modes of action, and biological pathways balancing on off-target and on-target effects. This approach may lead to the discovery of controllable array-designed therapies to combine less potent elements that are more effective collectively but have fewer adverse side effects than does any element singly.

Fragment-based approaches and computer-aided drug discovery

Fragment-based design has significantly modified drug discovery strategies and paradigms in the last decade. Besides technological advances and novel therapeutic avenues, one of the most significant changes brought by this new discipline has occurred in the minds of drug designers. Fragment-based approaches have markedly impacted rational computer-aided design both in method development and in applications. The present review illustrates the importance of molecular fragments in many aspects of rational ligand design, and discusses how thinking in "fragment space" has boosted computational biology and chemistry.

Introduction to fragment-based drug discovery

Fragment-based drug discovery (FBDD) has emerged in the past decade as a powerful tool for discovering drug leads. The approach first identifies starting points: very small molecules (fragments) that are about half the size of typical drugs. These fragments are then expanded or linked together to generate drug leads. Although the origins of the technique date back some 30 years, it was only in the mid-1990s that experimental techniques became sufficiently sensitive and rapid for the concept to be become practical. Since that time, the field has exploded: FBDD has played a role in discovery of at least 18 drugs that have entered the clinic, and practitioners of FBDD can be found throughout the world in both academia and industry. Literally dozens of reviews have been published on various aspects of FBDD or on the field as a whole, as have three books (Jahnke and Erlanson, Fragment-based approaches in drug discovery, 2006; Zartler and Shapiro, Fragment-based drug discovery: a practical approach, 2008; Kuo, Fragment based drug design: tools, practical approaches, and examples, 2011). However, this chapter will assume that the reader is approaching the field with little prior knowledge. It will introduce some of the key concepts, set the stage for the chapters to follow, and demonstrate how X-ray crystallography plays a central role in fragment identification and advancement.

A practical use of ligand efficiency indices out of the fragment-based approach: ligand efficiency-guided lead identification of soluble epoxide hydrolase inhibitors

Ligand efficiency is frequently used to evaluate fragment compounds in fragment-based drug discovery. We applied ligand efficiency indices in a conventional virtual screening-initiated lead generation study of soluble epoxide hydrolase inhibitors. From a considerable number of screening hits, we carefully selected a compound exhibiting relatively weak inhibitory activity but high ligand efficiency. This ligand efficiency-guided selection could reveal compounds possessing preferable lead-like characteristics in terms of molecular size and lipophilicity. The following hit-to-lead medicinal chemistry campaign successfully led to a more potent, ADMET-clean, lead-like compound preserving high ligand efficiency. Retrospective analyses, including consideration of the more recently proposed indices of ligand efficiency, shed light on the validity of our hit triage and hit-to-lead studies. The present work proposes a practical methodology for lead generation using the concept of ligand efficiency.

Are there differences between launched drugs, clinical candidates, and commercially available compounds?

To clarify the differences between commercially available compounds, clinical candidates, and launched drugs with regard to distribution of physicochemical properties and to characterize the correlation between physicochemical properties, we conducted analyses on physicochemical descriptors of commercially available compounds, clinical candidates, and launched drugs. Initial analysis of the marginal distribution of each physicochemical property showed that the distribution of commercially available compounds obeys a more normal distribution than that of launched drugs and clinical candidates. In addition, we calculated correlation coefficient values between values of physicochemical properties and found little similarity between values of clinical candidates and those of commercially available compounds, while observing marked similarity between values of clinical candidates and those of launched drugs. We also analyzed joint distribution for two physicochemical properties, with results showing that, similar to marginal distribution, the joint distribution of commercially available compounds obeys a more normal distribution than that of launched drugs and clinical candidates. We then assessed items using the Nagahara method, originally developed by one of this study's authors. Results showed that the probability distribution of molecular weight and log P for commercially available compounds was much narrower than that of launched drugs and clinical candidates. In conclusion, clinical candidates are more similar to launched drugs than to commercially available compounds with regard to marginal distribution, joint distribution, and correlation coefficients. These findings provide deeper insight regarding the concept of "druglikeness".

[Fragment-based drug discovery: concept and aim]

Fragment-Based Drug Discovery (FBDD) has been recognized as a newly emerging lead discovery methodology that involves biophysical fragment screening and chemistry-driven fragment-to-lead stages. Although fragments, defined as structurally simple and small compounds (typically <300 Da), have not been employed in conventional high-throughput screening (HTS), the recent significant progress in the biophysical screening methods enables fragment screening at a practical level. The intention of FBDD primarily turns our attention to weakly but specifically binding fragments (hit fragments) as the starting point of medicinal chemistry. Hit fragments are then promoted to more potent lead compounds through linking or merging with another hit fragment and/or attaching functional groups. Another positive aspect of FBDD is ligand efficiency. Ligand efficiency is a useful guide in screening hit selection and hit-to-lead phases to achieve lead-likeness. Owing to these features, a number of successful applications of FBDD to "undruggable targets" (where HTS and other lead identification methods failed to identify useful lead compounds) have been reported. As a result, FBDD is now expected to complement more conventional methodologies. This review, as an introduction of the following articles, will summarize the fundamental concepts of FBDD and will discuss its advantages over other conventional drug discovery approaches.