- Email: [email protected]
Recent advances in virtual screening for drug discovery
Methods 71 (2015) 1–3
Contents lists available at ScienceDirect
Methods journal homepage: www.elsevier.com/locate/ymeth
Recent advances in virtual screening for drug discovery Drug discovery is an expensive process, and taking a drug from development to the market can cost in excess of one billion US dollars and take over 10 years in time. However, the number of new drugs being approved for the market has not kept pace with the massive investment into pharmaceutical research. The highthroughput screening (HTS) of combinatorial libraries, which was thought in the 1990’s to herald a new era of drug discovery, has been increasingly found to be a remarkably inefficient method for lead identification. This has encouraged the development of chemical libraries based on diversity-oriented synthesis (DIOS) or biology-oriented synthesis (BIOS), which contain more diverse molecular scaffolds, or those based on natural products or approved drugs, which could be considered as privileged collections of bioactive structures. Another strategy that researchers have investigated to tackle the high cost of drug discovery is the use of in silico technologies. With exponential increases in computational power over the past few decades, the virtual screening of a chemical library now costs only a fraction of the time and money needed for the highthroughput screening of those compounds in vitro. The chief advantage of virtual screening is that inactive non-binders can be predicted in silico, hence eliminating the need for the synthesis and biological testing of those molecules. In other words, virtual screening can enrich libraries in molecules that are likely to bind to the biomolecular target of interest. Moreover, virtual screening can be used to prioritize molecules for synthesis or for the lead optimization of hit compounds. Today, virtual screening is routinely utilised for drug discovery and development in laboratories worldwide, particularly in academic environments where limitations on manpower and resources exist. This issue highlights recent advances in virtual screening and related techniques for drug discovery. Vrontaki, Melagraki, Mavromoustakos, and Afantitis have combined molecular docking, 3DQSAR CoMSIA and similarity searches into a multi-step framework to identify indole-based inhibitors of HCV replication from the ChEMBL database [1]. After docking 41 known inhibitors into the crystal structure of the HCV RNA-dependent RNA polymerase (NS5B GT1b), a validated 3D-QSAR CoMSIA model was developed to accurately predict activity values. The ChEMBL database was then mined to reveal indole-based compounds with high predicted biology activity. Muegge and Zhang have described a new virtual screening method that can handle upwards of trillions of individual molecules or tens of thousands of combinatorial libraries [2]. PharmShape is a three-dimensional ligand-based virtual screening tool that seeks to incorporate arguably the most important properties of small molecules for binding to a protein target: pharmacophore http://dx.doi.org/10.1016/j.ymeth.2014.12.012 1046-2023/Ó 2014 Elsevier Inc. All rights reserved.
and shape. On the other hand, PharmShapeCC is a combinatorial chemistry extension of PharmShape for the virtual screening of entire combinatorial libraries. To achieve high throughput, a number of filtering steps are applied. For example, conformations of a probe molecule are only generated if the molecule matches all of the pharmacophore features required, while the scoring function is only applied for those conformations that satisfy both the pharmacophore and shape queries. The performance of the program was demonstrated by the identification of a novel CXCR5 antagonist and novel chemotypes for CCR1, LTA4 hydrolase, and MMP13 from combinatorial hit libraries. Zhong, Lin, Tam, Lu, Chan, Ma and Leung have utilised highthroughput virtual screening to identify emodic acid and 6-chloroemodic acid as inhibitors of JAK2 from a natural product and natural product-like database of over 20,000 compounds [3]. Molecular docking conformations of these molecules revealed that they shared a similar binding pose to that of binding pose of CMP6, a known JAK2 inhibitor. Moreover, these molecules inhibited JAK2 enzyme activity in vitro, and blocked JAK2 autophosphorylation and STAT3 DNA-binding activity in cellulo. Kumar and Zhang have discussed the concept of a hierarchical virtual screening approach in small molecule drug discovery [4]. In hierarchical virtual screening, a large compound library is subjected to a sequential series of computational filters, such as similarity search, pharmacophore screening, and molecular docking, in order to generate a short-list of candidates for biological testing. Computational resources are preserved by utilising coarse ligandbased techniques at the start of hierarchical procedure, while computationally demanding structure-based methods are used towards the end. As no single ligand-based or structure-based technique performs equally well on all targets, the hierarchical combination of different computational methods can improve virtual screening performance by reducing the number of false positives, while enriching the remaining library in true hits. Leung, Liu, Lin, Lu, Zhong, Susanti, Rao, Wang, Che, Chan, Leung, Chan and Ma have identified a small-molecule inhibitor of STAT3 by ligand-based pharmacophore screening [5]. A training set of 9 STAT3 inhibitors binding to the SH2 domain was utilised to construct a pharmacophore model, which was used for the virtual screening of an in-house database of 78 compounds. An azepine derivative was subsequently found to inhibit STAT3 activity in vitro and STAT3-directed transcription in cellulo with comparable potency to the well-known STAT3 inhibitor S3I-201. A fluorescence polarization assay revealed that compound 1 targeted the SH2 domain of STAT3, validating the use of pharmacophore modelling to identify inhibitors of protein–protein interactions.
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Editorial / Methods 71 (2015) 1–3
Westermaier, Barril and Scapozza have reviewed the approach of inverse virtual screening (IVS) as an efficient strategy for the interrogation of the proteome with small molecules [6]. Compared to the well-known technique of (forward) virtual screening, which screens a protein structure against a large chemical database, IVS can offer a complementary approach for target identification, protein deorphanisation and drug repurposing applications. The strengths and weaknesses of IVS are highlighted and discussed in the fields of chemical genomics, target discovery and drug design. Moreover, inverse screening validation and target prioritization strategies are presented. Cereto-Massagué, Ojeda, Valls, Mulero, Garcia-Vallvé and Pujadas present a review on the use of molecular fingerprint similarity searching in virtual screening [7]. Molecular fingerprints encode particular aspects of molecular structure, and can be rapidly mined in substructure or similarity searches to identify hit compounds with the desired characteristics. This review describes popular fingerprint algorithms and their uses, as well as the software packages and online tools that offer these algorithms. Kumar, Krishna and Siddiqi discuss both structure-based and ligand-based virtual screening techniques for the discovery of anti-cancer drugs [8]. Recent examples of these techniques for the discovery of potential anti-cancer agents with activities in the nanomolar range are described. NEDD8-activating enzyme (NAE) mediates the specific degradation of proteins regulated by cullin-RING ubiquitin E3 ligases, and has been considered as an attractive target for the development of anti-cancer agents. Zhong, Leung, Lin, Chan, Han, Chan, Ma and Leung have utilised a pharmacophore screening method to identify deoxyvasicinone-based derivatives against NAE [9]. Two top analogues identified from the virtual screening campaign selectively inhibited NAE activity in both cell-free and cell-based systems. Moreover, molecular modelling analysis suggested that the compounds target the ATP-binding domain of NAE. The application of structure-based virtual screening has typically relied on the availability of an experimentally-derived protein structure or that of a close homologue template so that a high-resolution model can be constructed. Using the DUD database of structural decoys, Du, Brender, Zhang and Zhang have shown that templates with only weak sequence homology can often be used to construct structural models to achieve comparable enrichment rates to experimental crystal structures [10]. The results of this study suggest that protein structure modelling methods can generate acceptable models for structure-based docking, which could represent a valuable technique for targeting proteins that lack crystallographic structures. Hoi, Li, Vong, Tseng, Kwan and Lee have reviewed the application of structure-based drug design and virtual screening of VEGFR tyrosine kinase inhibitors [11]. Recent studies on different structures of VEGFR2 are discussed, which reveal special insights into the molecular interactions between VEGFR2 with diverse inhibitors. Furthermore, recent examples of virtual screening, lead optimization and structure-based design of VEGFR2 inhibitors are described. Liu, Leung, Lin, Chan, Susanti, Rao, Chan, Ma and Leung have employed pharmacophore modelling for the identification of small-molecule inhibitors of TACE [12]. A pharmacophore model constructed from a training set of 15 TACE inhibitors with diverse structures. The top compound identified from the screening campaign was found to inhibit TACE enzymatic activity and block the production of soluble TNF-a in the human acute monocytic leukemia THP-1 cell line, and also showed anti-proliferative activity against THP-1 cells.
Cereto-Massagué, Ojeda, Valls, Mulero, Pujadas, and GarciaVallve review the technique of target fishing, also known as reverse screening or reverse pharmacognosy, in which the most likely biomolecular targets of a probe molecular are identified [13]. The authors describe the different methods utilised for target prediction, the bioactivity databases most commonly employed, and the available programs and servers that can be used to generate these types of predictions. G-protein-coupled receptors (GPCRs) are membrane proteins that respond to a variety of ligands, and have essential roles in a wide range of biological processes. Fidom and co-workers have reported a new pharmacophore-based method for GPCRs that involves the extraction of structural fragments, which are interacting ligand moiety and receptor residue pairs, from crystal structure complexes [14]. By constructing a library with over 250 fragments covering 29 residue positions within the generic transmembrane binding pocket, this fragment-based method has the important advantage that it can generate pharmacophores for GPCRs for which no (homologous) crystal structures or ligands are available. This methodology was validated by the virtual screening of histamine H1 and H3 receptor pharmacophores. Vuorinen and Schuster describe methods for the generation and application of pharmacophore models as virtual screening filters and for bioactivity profiling [15]. Methodological details regarding dataset generation, 3D-representations and conformational analysis, pharmacophore model construction, model validation are presented. At the other end of the spectrum, Danishuddin and Khan have provided an updated survey of structure-based virtual screening in drug discovery [16]. Various aspects of structurebased virtual screening are described, and recent successful cases are highlighted. Retrospective small-scale virtual screening on benchmarking data sets is a method that is widely used to estimate ligand enrichment in prospective virtual screening efforts. Xia, Tilahun, Reid, Zhang and Wang have undertaken a comparison of different benchmarking methods to identify possible sources of bias in this analysis [17]. The authors identified three types of bias, termed analogue bias, artificial enrichment and false negative, and also introduce a new algorithm to construct maximum-unbiased benchmarking sets that can be used for both ligand-based and structure-based virtual screening analysis. Epigenetic modifications are an expanding area of research interest due to their important effects on both normal and disease processes. Li, Yang, Yuan, Zou, Cao, Yang, Xiang and Xiang have reviewed the application of virtual screening techniques to identify inhibitors against epigenetic targets [18]. The studies and reviews in this issue represent the latest developments in the field of virtual screening for drug discovery. It is hoped that the readers of this issue will be able to gain insight into the applications offered by this emerging technique, and possibly implement certain strategies in their own research. References [1] E. Vrontaki, G. Melagraki, T. Mavromoustakos, A. Afantitis, Methods 70 (2–3) (2014) 4–13. [2] I. Muegge, Q. Zhang, Methods 70 (2–3) (2014) 14–20. [3] H.J. Zhong, S. Lin, I.L. Tam, L. Lu, D.S.H. Chan, D.L. Ma, C.H. Leung, Methods 70 (2–3) (2014) 21–25. [4] A. Kumar, K.Y.J. Zhang, Methods 70 (2–3) (2014) 26–37. [5] K.H. Leung, L.J. Liu, S. Lin, L. Lu, H.J. Zhong, D. Susanti, W. Rao, M. Wang, W.I. Che, D.S.H. Chan, C.H. Leung, P.W.H. Chan, D.L. Ma, Methods 70 (2–3) (2014) 38–43. [6] Y. Westermaier, X. Barril, L. Scapozza, Methods 70 (2–3) (2014) 44–57. [7] A. Cereto-Massagué, M.J. Ojeda, C. Valls, M. Mulero, S. Garcia-Vallvé, G. Pujadas, Methods 70 (2–3) (2014) 58–63. [8] V. Kumar, S. Krishna, M.I. Siddiqi, Methods 70 (2–3) (2014) 64–70.
Editorial / Methods 71 (2015) 1–3 [9] H.J. Zhong, K.H. Leung, S. Lin, D.S.H. Chan, Q.B. Han, S.L.F. Chan, D.L. Ma, C.H. Leung, Methods 70 (2–3) (2014) 71–76. [10] H. Du, J.R. Brender, J. Zhang, Y. Zhang, Methods 70 (2–3) (2014) 77–84. [11] P.M. Hoi, S. Li, C.T. Vong, H.H.L. Tseng, Y.W. Kwan, S.M.Y. Lee, Methods 70 (2–3) (2014) 85–91. [12] L.J. Liu, K.H. Leung, S. Lin, D.S.H. Chan, D. Susanti, W. Rao, P.W.H. Chan, D.L. Ma, C.H. Leung, Methods 70 (2–3) (2014) 92–97. [13] A. Cereto-Massagué, J.J. Ojeda, C. Valls, M. Mulero, G. Pujadas, S. Garcia-Vallve, Methods 70 (2–3) (2014) 98–103. [14] K. Fidom, V. Isberg, A.S. Hauser, S. Mordalski, T. Lehto, A.J. Bojarski, D.E. Gloriam, Methods 70 (2–3) (2014) 104–112. [15] A. Vuorinen, D. Schuster, Methods 70 (2–3) (2014) 113–134. [16] M. Danishuddin, A.U. Khan, Methods 70 (2–3) (2014) 135–145. [17] J. Xia, E.L. Tilahun, T.E. Reid, L. Zhang, X.S. Wang, Methods 70 (2–3) (2014) 146–157.
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[18] G.B. Li, L.L. Yang, Y. Yuan, J. Zou, Y. Cao, S.Y. Yang, R. Xiang, M. Xiang, Methods 70 (2–3) (2014) 158–166.
Editors Chung-Hang Leung Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau, China E-mail address: [email protected] Dik-Lung Ma Department of Chemistry, Hong Kong Baptist University, Hong Kong E-mail address: [email protected]
Contents lists available at ScienceDirect
Methods journal homepage: www.elsevier.com/locate/ymeth
Recent advances in virtual screening for drug discovery Drug discovery is an expensive process, and taking a drug from development to the market can cost in excess of one billion US dollars and take over 10 years in time. However, the number of new drugs being approved for the market has not kept pace with the massive investment into pharmaceutical research. The highthroughput screening (HTS) of combinatorial libraries, which was thought in the 1990’s to herald a new era of drug discovery, has been increasingly found to be a remarkably inefficient method for lead identification. This has encouraged the development of chemical libraries based on diversity-oriented synthesis (DIOS) or biology-oriented synthesis (BIOS), which contain more diverse molecular scaffolds, or those based on natural products or approved drugs, which could be considered as privileged collections of bioactive structures. Another strategy that researchers have investigated to tackle the high cost of drug discovery is the use of in silico technologies. With exponential increases in computational power over the past few decades, the virtual screening of a chemical library now costs only a fraction of the time and money needed for the highthroughput screening of those compounds in vitro. The chief advantage of virtual screening is that inactive non-binders can be predicted in silico, hence eliminating the need for the synthesis and biological testing of those molecules. In other words, virtual screening can enrich libraries in molecules that are likely to bind to the biomolecular target of interest. Moreover, virtual screening can be used to prioritize molecules for synthesis or for the lead optimization of hit compounds. Today, virtual screening is routinely utilised for drug discovery and development in laboratories worldwide, particularly in academic environments where limitations on manpower and resources exist. This issue highlights recent advances in virtual screening and related techniques for drug discovery. Vrontaki, Melagraki, Mavromoustakos, and Afantitis have combined molecular docking, 3DQSAR CoMSIA and similarity searches into a multi-step framework to identify indole-based inhibitors of HCV replication from the ChEMBL database [1]. After docking 41 known inhibitors into the crystal structure of the HCV RNA-dependent RNA polymerase (NS5B GT1b), a validated 3D-QSAR CoMSIA model was developed to accurately predict activity values. The ChEMBL database was then mined to reveal indole-based compounds with high predicted biology activity. Muegge and Zhang have described a new virtual screening method that can handle upwards of trillions of individual molecules or tens of thousands of combinatorial libraries [2]. PharmShape is a three-dimensional ligand-based virtual screening tool that seeks to incorporate arguably the most important properties of small molecules for binding to a protein target: pharmacophore http://dx.doi.org/10.1016/j.ymeth.2014.12.012 1046-2023/Ó 2014 Elsevier Inc. All rights reserved.
and shape. On the other hand, PharmShapeCC is a combinatorial chemistry extension of PharmShape for the virtual screening of entire combinatorial libraries. To achieve high throughput, a number of filtering steps are applied. For example, conformations of a probe molecule are only generated if the molecule matches all of the pharmacophore features required, while the scoring function is only applied for those conformations that satisfy both the pharmacophore and shape queries. The performance of the program was demonstrated by the identification of a novel CXCR5 antagonist and novel chemotypes for CCR1, LTA4 hydrolase, and MMP13 from combinatorial hit libraries. Zhong, Lin, Tam, Lu, Chan, Ma and Leung have utilised highthroughput virtual screening to identify emodic acid and 6-chloroemodic acid as inhibitors of JAK2 from a natural product and natural product-like database of over 20,000 compounds [3]. Molecular docking conformations of these molecules revealed that they shared a similar binding pose to that of binding pose of CMP6, a known JAK2 inhibitor. Moreover, these molecules inhibited JAK2 enzyme activity in vitro, and blocked JAK2 autophosphorylation and STAT3 DNA-binding activity in cellulo. Kumar and Zhang have discussed the concept of a hierarchical virtual screening approach in small molecule drug discovery [4]. In hierarchical virtual screening, a large compound library is subjected to a sequential series of computational filters, such as similarity search, pharmacophore screening, and molecular docking, in order to generate a short-list of candidates for biological testing. Computational resources are preserved by utilising coarse ligandbased techniques at the start of hierarchical procedure, while computationally demanding structure-based methods are used towards the end. As no single ligand-based or structure-based technique performs equally well on all targets, the hierarchical combination of different computational methods can improve virtual screening performance by reducing the number of false positives, while enriching the remaining library in true hits. Leung, Liu, Lin, Lu, Zhong, Susanti, Rao, Wang, Che, Chan, Leung, Chan and Ma have identified a small-molecule inhibitor of STAT3 by ligand-based pharmacophore screening [5]. A training set of 9 STAT3 inhibitors binding to the SH2 domain was utilised to construct a pharmacophore model, which was used for the virtual screening of an in-house database of 78 compounds. An azepine derivative was subsequently found to inhibit STAT3 activity in vitro and STAT3-directed transcription in cellulo with comparable potency to the well-known STAT3 inhibitor S3I-201. A fluorescence polarization assay revealed that compound 1 targeted the SH2 domain of STAT3, validating the use of pharmacophore modelling to identify inhibitors of protein–protein interactions.
2
Editorial / Methods 71 (2015) 1–3
Westermaier, Barril and Scapozza have reviewed the approach of inverse virtual screening (IVS) as an efficient strategy for the interrogation of the proteome with small molecules [6]. Compared to the well-known technique of (forward) virtual screening, which screens a protein structure against a large chemical database, IVS can offer a complementary approach for target identification, protein deorphanisation and drug repurposing applications. The strengths and weaknesses of IVS are highlighted and discussed in the fields of chemical genomics, target discovery and drug design. Moreover, inverse screening validation and target prioritization strategies are presented. Cereto-Massagué, Ojeda, Valls, Mulero, Garcia-Vallvé and Pujadas present a review on the use of molecular fingerprint similarity searching in virtual screening [7]. Molecular fingerprints encode particular aspects of molecular structure, and can be rapidly mined in substructure or similarity searches to identify hit compounds with the desired characteristics. This review describes popular fingerprint algorithms and their uses, as well as the software packages and online tools that offer these algorithms. Kumar, Krishna and Siddiqi discuss both structure-based and ligand-based virtual screening techniques for the discovery of anti-cancer drugs [8]. Recent examples of these techniques for the discovery of potential anti-cancer agents with activities in the nanomolar range are described. NEDD8-activating enzyme (NAE) mediates the specific degradation of proteins regulated by cullin-RING ubiquitin E3 ligases, and has been considered as an attractive target for the development of anti-cancer agents. Zhong, Leung, Lin, Chan, Han, Chan, Ma and Leung have utilised a pharmacophore screening method to identify deoxyvasicinone-based derivatives against NAE [9]. Two top analogues identified from the virtual screening campaign selectively inhibited NAE activity in both cell-free and cell-based systems. Moreover, molecular modelling analysis suggested that the compounds target the ATP-binding domain of NAE. The application of structure-based virtual screening has typically relied on the availability of an experimentally-derived protein structure or that of a close homologue template so that a high-resolution model can be constructed. Using the DUD database of structural decoys, Du, Brender, Zhang and Zhang have shown that templates with only weak sequence homology can often be used to construct structural models to achieve comparable enrichment rates to experimental crystal structures [10]. The results of this study suggest that protein structure modelling methods can generate acceptable models for structure-based docking, which could represent a valuable technique for targeting proteins that lack crystallographic structures. Hoi, Li, Vong, Tseng, Kwan and Lee have reviewed the application of structure-based drug design and virtual screening of VEGFR tyrosine kinase inhibitors [11]. Recent studies on different structures of VEGFR2 are discussed, which reveal special insights into the molecular interactions between VEGFR2 with diverse inhibitors. Furthermore, recent examples of virtual screening, lead optimization and structure-based design of VEGFR2 inhibitors are described. Liu, Leung, Lin, Chan, Susanti, Rao, Chan, Ma and Leung have employed pharmacophore modelling for the identification of small-molecule inhibitors of TACE [12]. A pharmacophore model constructed from a training set of 15 TACE inhibitors with diverse structures. The top compound identified from the screening campaign was found to inhibit TACE enzymatic activity and block the production of soluble TNF-a in the human acute monocytic leukemia THP-1 cell line, and also showed anti-proliferative activity against THP-1 cells.
Cereto-Massagué, Ojeda, Valls, Mulero, Pujadas, and GarciaVallve review the technique of target fishing, also known as reverse screening or reverse pharmacognosy, in which the most likely biomolecular targets of a probe molecular are identified [13]. The authors describe the different methods utilised for target prediction, the bioactivity databases most commonly employed, and the available programs and servers that can be used to generate these types of predictions. G-protein-coupled receptors (GPCRs) are membrane proteins that respond to a variety of ligands, and have essential roles in a wide range of biological processes. Fidom and co-workers have reported a new pharmacophore-based method for GPCRs that involves the extraction of structural fragments, which are interacting ligand moiety and receptor residue pairs, from crystal structure complexes [14]. By constructing a library with over 250 fragments covering 29 residue positions within the generic transmembrane binding pocket, this fragment-based method has the important advantage that it can generate pharmacophores for GPCRs for which no (homologous) crystal structures or ligands are available. This methodology was validated by the virtual screening of histamine H1 and H3 receptor pharmacophores. Vuorinen and Schuster describe methods for the generation and application of pharmacophore models as virtual screening filters and for bioactivity profiling [15]. Methodological details regarding dataset generation, 3D-representations and conformational analysis, pharmacophore model construction, model validation are presented. At the other end of the spectrum, Danishuddin and Khan have provided an updated survey of structure-based virtual screening in drug discovery [16]. Various aspects of structurebased virtual screening are described, and recent successful cases are highlighted. Retrospective small-scale virtual screening on benchmarking data sets is a method that is widely used to estimate ligand enrichment in prospective virtual screening efforts. Xia, Tilahun, Reid, Zhang and Wang have undertaken a comparison of different benchmarking methods to identify possible sources of bias in this analysis [17]. The authors identified three types of bias, termed analogue bias, artificial enrichment and false negative, and also introduce a new algorithm to construct maximum-unbiased benchmarking sets that can be used for both ligand-based and structure-based virtual screening analysis. Epigenetic modifications are an expanding area of research interest due to their important effects on both normal and disease processes. Li, Yang, Yuan, Zou, Cao, Yang, Xiang and Xiang have reviewed the application of virtual screening techniques to identify inhibitors against epigenetic targets [18]. The studies and reviews in this issue represent the latest developments in the field of virtual screening for drug discovery. It is hoped that the readers of this issue will be able to gain insight into the applications offered by this emerging technique, and possibly implement certain strategies in their own research. References [1] E. Vrontaki, G. Melagraki, T. Mavromoustakos, A. Afantitis, Methods 70 (2–3) (2014) 4–13. [2] I. Muegge, Q. Zhang, Methods 70 (2–3) (2014) 14–20. [3] H.J. Zhong, S. Lin, I.L. Tam, L. Lu, D.S.H. Chan, D.L. Ma, C.H. Leung, Methods 70 (2–3) (2014) 21–25. [4] A. Kumar, K.Y.J. Zhang, Methods 70 (2–3) (2014) 26–37. [5] K.H. Leung, L.J. Liu, S. Lin, L. Lu, H.J. Zhong, D. Susanti, W. Rao, M. Wang, W.I. Che, D.S.H. Chan, C.H. Leung, P.W.H. Chan, D.L. Ma, Methods 70 (2–3) (2014) 38–43. [6] Y. Westermaier, X. Barril, L. Scapozza, Methods 70 (2–3) (2014) 44–57. [7] A. Cereto-Massagué, M.J. Ojeda, C. Valls, M. Mulero, S. Garcia-Vallvé, G. Pujadas, Methods 70 (2–3) (2014) 58–63. [8] V. Kumar, S. Krishna, M.I. Siddiqi, Methods 70 (2–3) (2014) 64–70.
Editorial / Methods 71 (2015) 1–3 [9] H.J. Zhong, K.H. Leung, S. Lin, D.S.H. Chan, Q.B. Han, S.L.F. Chan, D.L. Ma, C.H. Leung, Methods 70 (2–3) (2014) 71–76. [10] H. Du, J.R. Brender, J. Zhang, Y. Zhang, Methods 70 (2–3) (2014) 77–84. [11] P.M. Hoi, S. Li, C.T. Vong, H.H.L. Tseng, Y.W. Kwan, S.M.Y. Lee, Methods 70 (2–3) (2014) 85–91. [12] L.J. Liu, K.H. Leung, S. Lin, D.S.H. Chan, D. Susanti, W. Rao, P.W.H. Chan, D.L. Ma, C.H. Leung, Methods 70 (2–3) (2014) 92–97. [13] A. Cereto-Massagué, J.J. Ojeda, C. Valls, M. Mulero, G. Pujadas, S. Garcia-Vallve, Methods 70 (2–3) (2014) 98–103. [14] K. Fidom, V. Isberg, A.S. Hauser, S. Mordalski, T. Lehto, A.J. Bojarski, D.E. Gloriam, Methods 70 (2–3) (2014) 104–112. [15] A. Vuorinen, D. Schuster, Methods 70 (2–3) (2014) 113–134. [16] M. Danishuddin, A.U. Khan, Methods 70 (2–3) (2014) 135–145. [17] J. Xia, E.L. Tilahun, T.E. Reid, L. Zhang, X.S. Wang, Methods 70 (2–3) (2014) 146–157.
3
[18] G.B. Li, L.L. Yang, Y. Yuan, J. Zou, Y. Cao, S.Y. Yang, R. Xiang, M. Xiang, Methods 70 (2–3) (2014) 158–166.
Editors Chung-Hang Leung Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau, China E-mail address: [email protected] Dik-Lung Ma Department of Chemistry, Hong Kong Baptist University, Hong Kong E-mail address: [email protected]