A guide to drug discovery: Hit and lead generation: beyond high-throughput screening

The identification of small-molecule modulators of protein function, and the process of transforming these into high-content lead series, are key activities in modern drug discovery. The decisions taken during this process have far-reaching consequences for success later in lead optimization and even more crucially in clinical development. Recently, there has been an increased focus on these activities due to escalating downstream costs resulting from high clinical failure rates. In addition, the vast emerging opportunities from efforts in functional genomics and proteomics demands a departure from the linear process of identification, evaluation and refinement activities towards a more integrated parallel process. This calls for flexible, fast and cost-effective strategies to meet the demands of producing high-content lead series with improved prospects for clinical success.

[1]  Jürgen Bajorath,et al.  Integration of virtual and high-throughput screening , 2002, Nature Reviews Drug Discovery.

[2]  International Human Genome Sequencing Consortium Initial sequencing and analysis of the human genome , 2001, Nature.

[3]  Philip M. Dean,et al.  A validation study on the practical use of automated de novo design , 2002, J. Comput. Aided Mol. Des..

[4]  Johan Schultz,et al.  Structure-based screening and design in drug discovery. , 2002, Drug discovery today.

[5]  John Hodgson,et al.  ADMET—turning chemicals into drugs , 2001, Nature Biotechnology.

[6]  B. O'dowd,et al.  Identification of four novel human G protein-coupled receptors expressed in the brain. , 2001, Brain research. Molecular brain research.

[7]  J. Drews Drug discovery: a historical perspective. , 2000, Science.

[8]  Wolfgang Guba,et al.  Development of a virtual screening method for identification of "frequent hitters" in compound libraries. , 2002, Journal of medicinal chemistry.

[9]  Andrew R. Leach,et al.  Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery , 2001, J. Chem. Inf. Comput. Sci..

[10]  Peter J. Alaimo,et al.  Chemical genetic approaches for the elucidation of signaling pathways. , 2001, Current opinion in chemical biology.

[11]  J. Proudfoot Drugs, leads, and drug-likeness: an analysis of some recently launched drugs. , 2002, Bioorganic & medicinal chemistry letters.

[12]  D. Rognan,et al.  Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. , 2000, Journal of medicinal chemistry.

[13]  K. Bleicher,et al.  Chemogenomics: bridging a drug discovery gap. , 2002, Current medicinal chemistry.

[14]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.

[15]  H. Kubinyi,et al.  A scoring scheme for discriminating between drugs and nondrugs. , 1998, Journal of medicinal chemistry.

[16]  P. Sprague Automated chemical hypothesis generation and database searching with Catalyst , 1995 .

[17]  W Patrick Walters,et al.  Prediction of 'drug-likeness'. , 2002, Advanced drug delivery reviews.

[18]  A. Hopkins,et al.  The druggable genome , 2002, Nature Reviews Drug Discovery.

[19]  H. Jhoti,et al.  Structure-based screening of low-affinity compounds. , 2002, Drug discovery today.

[20]  Kirsch,et al.  Virtual Screening for Bioactive Molecules by Evolutionary De Novo Design Special thanks to Neil R. Taylor for his help in preparation of the manuscript. , 2000, Angewandte Chemie.

[21]  Gisbert Schneider,et al.  A Virtual Screening Method for Prediction of the hERG Potassium Channel Liability of Compound Libraries , 2002, Chembiochem : a European journal of chemical biology.

[22]  Gisbert Schneider,et al.  Virtual screening and fast automated docking methods. , 2002, Drug discovery today.

[23]  J. Ellman,et al.  Identification of potent and selective mechanism-based inhibitors of the cysteine protease cruzain using solid-phase parallel synthesis. , 2002, Journal of medicinal chemistry.

[24]  Schmid,et al.  "Scaffold-Hopping" by Topological Pharmacophore Search: A Contribution to Virtual Screening. , 1999, Angewandte Chemie.

[25]  Arthur A. Patchett,et al.  Chapter 26. Privileged structures — An update , 2000 .

[26]  G. Schneider,et al.  Virtual Screening for Bioactive Molecules , 2000 .

[27]  Gisbert Schneider,et al.  A fast virtual screening filter for cytochrome P450 3A4 inhibition liability of compound libraries , 2002 .

[28]  Hans-Joachim Böhm,et al.  Prediction of binding constants of protein ligands: A fast method for the prioritization of hits obtained from de novo design or 3D database search programs , 1998, J. Comput. Aided Mol. Des..

[29]  Jonathan S. Mason,et al.  Chemistry Space Metrics in Diversity Analysis, Library Design, and Compound Selection , 1998, J. Chem. Inf. Comput. Sci..

[30]  Lenz,et al.  Chemical ligands, genomics and drug discovery. , 2000, Drug discovery today.

[31]  Roger E. Critchlow,et al.  Beyond mere diversity: tailoring combinatorial libraries for drug discovery. , 1999, Journal of combinatorial chemistry.

[32]  Neal F. Cariello,et al.  Comparison of the computer programs DEREK and TOPKAT to predict bacterial mutagenicity. Deductive Estimate of Risk from Existing Knowledge. Toxicity Prediction by Komputer Assisted Technology. , 2002, Mutagenesis.

[33]  Alexander Alanine,et al.  Lead generation--enhancing the success of drug discovery by investing in the hit to lead process. , 2003, Combinatorial chemistry & high throughput screening.

[34]  Malcolm J. McGregor,et al.  Clustering of Large Databases of Compounds: Using the MDL "Keys" as Structural Descriptors , 1997, J. Chem. Inf. Comput. Sci..

[35]  P. Sanseau,et al.  In silico identification of novel therapeutic targets. , 2002, Drug discovery today.

[36]  David T. Stanton,et al.  Evaluation and Use of BCUT Descriptors in QSAR and QSPR Studies , 1999, J. Chem. Inf. Comput. Sci..

[37]  K. Bleicher,et al.  Parallel solution- and solid-phase synthesis of spiropyrrolo-pyrroles as novel neurokinin receptor ligands. , 2002, Bioorganic & medicinal chemistry letters.

[38]  Tudor I. Oprea,et al.  The Design of Leadlike Combinatorial Libraries. , 1999, Angewandte Chemie.

[39]  C. Zechel,et al.  Combinatorial Synthesis of Small Organic Molecules , 1996 .

[40]  Bohdan Waszkowycz,et al.  PRO_SELECT: combining structure-based drug design and array-based chemistry for rapid lead discovery. 2. The development of a series of highly potent and selective factor Xa inhibitors. , 2002, Journal of medicinal chemistry.

[41]  A. Good,et al.  3-D pharmacophores in drug discovery. , 2001, Current pharmaceutical design.