Hit Triage Using Efficiency Indices after Screening of Compound Libraries in Drug Discovery

Reitz, Allen B.; Smith, Garry R.; Tounge, Brett A.; Reynolds, Charles H.

doi:10.2174/156802609790102365

Cited by 15 publications

(15 citation statements)

References 28 publications

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“…The concept of ligand efficiency is a simple way to merge binding and pharmacokinetic characteristics of a ligand into a single measure. EI has already been applied in many studies and it is suggested to become a useful tool of fragment-based drug discovery [102,104,[107][108][109], lead optimization [46], and drug chemical (molecular) space localization for some diseases or organs [110]…”

Section: Ei Lipo = Pic 50 (Or Pk I ) -Clogp (Or Logd)mentioning

confidence: 99%

Molecular Property Filters Describing Pharmacokinetics and Drug Binding

García-Sosa

Maran

Hetényi

2012

CMC

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Drug-target binding affinity and pharmacokinetics are equally important factors of drug design. Simple molecular properties such as molecular size have been used as pharmacokinetic and/or drug-likeness filters during chemical library design and also correlated with binding affinity. In the present study, current property filters are reviewed, a collection of their optimal values is provided, and a statistical framework is introduced allowing calibration of their selectivity and sensitivity for drugs. The role of ligand efficiency indices in drug design is also described. It is concluded that the usefulness of property filters of molecular size and lipophilicity is limited as predictors of general drug-likeness. However, they demonstrate increased performance in specific cases, e.g. in central nervous system diseases, emphasizing their future importance in specific, disease-focused library design instead of general drug-likeness filtering.

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Section: Ei Lipo = Pic 50 (Or Pk I ) -Clogp (Or Logd)mentioning

confidence: 99%

Molecular Property Filters Describing Pharmacokinetics and Drug Binding

García-Sosa

Maran

Hetényi

2012

CMC

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“…A set of 36 compounds was selected for further evaluation based on LE (LE > 0.40) and tractability. 15,16 Tractability was a subjective measure and was influenced by structural novelty, ease of synthesis, and the ability to introduce substituents in multiple vectors for optimization. Representative examples are listed in Table 1.…”

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confidence: 99%

Aminoindazole PDK1 Inhibitors: A Case Study in Fragment-Based Drug Discovery

Medina¹,

Blackledge²,

Heerding³

et al. 2010

ACS Med. Chem. Lett.

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Fragment screening of phosphoinositide-dependent kinase-1 (PDK1) in a biochemical kinase assay afforded hits that were characterized and prioritized based on ligand efficiency and binding interactions with PDK1 as determined by NMR. Subsequent crystallography and follow-up screening led to the discovery of aminoindazole 19, a potent leadlike PDK1 inhibitor with high ligand efficiency. Well-defined structure-activity relationships and protein crystallography provide a basis for further elaboration and optimization of 19 as a PDK1 inhibitor.KEYWORDS Aminoindazole PDK1 inhibitors, fragment-based drug discovery, ligand efficiency, binding interactions F ragment-based drug discovery (FBDD) is widely recognized as an alternative to high-throughput screening for hit identification.1-3 Success in implementing FBDD to deliver clinical candidates supports FBDD as a valuable and proven strategy in medicinal chemistry. 4,5 Endeavors in FBDD begin with screening collections of small, low molecular weight molecules (generally <250 MW) for inhibition or binding to a therapeutic target. This offers a broad coverage of chemical space, high hit rates, and quality starting points for hit-to-lead campaigns. Using principles of FBDD and ligand efficiency (LE), the medicinal chemist can accurately define structure-activity relationships (SAR) and measure progress during lead optimization.6 Once a hit is confirmed in a FBDD campaign, the medicinal chemist is confronted with fragment expansion or evolution, a difficult but necessary step to bridge the gap from fragment to leadlike molecule. 7 This report is an account of our success using a FBDD approach to identify and progress fragment inhibitors of phosphoinositide-dependent kinase-1 (PDK1), an integral component of the PI3K/AKT/ mTOR pathway, which is one of the most commonly deregulated signaling pathways across all cancers. 8-11The composition of the fragment library used in the PDK1 screen was biased toward molecules with donor and/or acceptor motifs embedded in an aromatic ring, which could fill a flat lipophilic pocket and engage the kinase hinge through hydrogen-bonding interactions, similar to the adenine ring of ATP. 12The assembled library consisted of 1065 fragments originating from our proprietary compound collection and external sources. We began our screen for PDK1 inhibitors using a biochemical kinase assay at high fragment concentration (400 μM).13 We subsequently selected 193 compounds for IC 50 determination based on percent inhibition (g60%) and chemical purity14 resulting in 89 compounds with IC 50 < 400 μM. A set of 36 compounds was selected for further evaluation based on LE (LE > 0.40) and tractability. 15,16 Tractability was a subjective measure and was influenced by structural novelty, ease of synthesis, and the ability to introduce substituents in multiple vectors for optimization. Representative examples are listed in Table 1.Saturation transfer difference (STD) experiments 17 were used to confirm an interaction of the hits with PDK1. This allowed...

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“…Such as the Compound 52 has higher pIC 50 value than Compounds 55-59. Compared with Figure 10, the SEI contour map ( Figure 11) is same in regions A and B and a little discrepancy in regions C. No other material could provide the special structure in region C, so the large group affection in SEI-based model may result from the extension of group A. Analyzing the atomic group in region C, the bulky group can introduce large TPSA in SEI, so the value of SEI tends to be little in the affection of region C, such as Compounds 19,22,23,24,[26][27][28][29][30][31][32][33][34][35][37][38][39][40]43, 47 and 54.…”

Section: Comfa Contour Maps and Model Comparisonmentioning

confidence: 99%

Ligand Efficiency Outperforms pIC₅₀ on Both 2D MLR and 3D CoMFA Models: A Case Study on AR Antagonists

Bai

Liu

et al. 2015

Chem Biol Drug Des

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The concept of ligand efficiency is defined as biological activity in each molecular size and is widely accepted throughout the drug design community. Among different LE indices, surface efficiency index (SEI) was reported to be the best one in support vector machine modeling, much better than the generally and traditionally used end-point pIC50. In this study, 2D multiple linear regression and 3D comparative molecular field analysis methods are employed to investigate the structure-activity relationships of a series of androgen receptor antagonists, using pIC50 and SEI as dependent variables to verify the influence of using different kinds of end-points. The obtained results suggest that SEI outperforms pIC50 on both MLR and CoMFA models with higher stability and predictive ability. After analyzing the characteristics of the two dependent variables SEI and pIC50, we deduce that the superiority of SEI maybe lie in that SEI could reflect the relationship between molecular structures and corresponding bioactivities, in nature, better than pIC50. This study indicates that SEI could be a more rational parameter to be optimized in the drug discovery process than pIC50.

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Hit Triage Using Efficiency Indices after Screening of Compound Libraries in Drug Discovery

Cited by 15 publications

References 28 publications

Molecular Property Filters Describing Pharmacokinetics and Drug Binding

Molecular Property Filters Describing Pharmacokinetics and Drug Binding

Aminoindazole PDK1 Inhibitors: A Case Study in Fragment-Based Drug Discovery

Ligand Efficiency Outperforms pIC₅₀ on Both 2D MLR and 3D CoMFA Models: A Case Study on AR Antagonists

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Hit Triage Using Efficiency Indices after Screening of Compound Libraries in Drug Discovery

Cited by 15 publications

References 28 publications

Molecular Property Filters Describing Pharmacokinetics and Drug Binding

Molecular Property Filters Describing Pharmacokinetics and Drug Binding

Aminoindazole PDK1 Inhibitors: A Case Study in Fragment-Based Drug Discovery

Ligand Efficiency Outperforms pIC50 on Both 2D MLR and 3D CoMFA Models: A Case Study on AR Antagonists

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Ligand Efficiency Outperforms pIC₅₀ on Both 2D MLR and 3D CoMFA Models: A Case Study on AR Antagonists