Literature DB >> 12489682

Current trends in lead discovery: are we looking for the appropriate properties?

Tudor I Oprea1.   

Abstract

The new drug discovery paradigm is based on high-throughput technologies, both with respect to synthesis and screening. The progression HTS hits --> lead series --> candidate drug --> marketed drug appears to indicate that the probability of reaching launched status is one in a million. This has shifted the focus from good quality candidate drugs to good quality leads. We examined the current trends in lead discovery by comparing MW (molecular weight), LogP (octanol/water partition coefficient, estimated by Kowwin) and LogSw (intrinsic water solubility, estimated by Wskowwin) for the following categories: 62 leads and 75 drugs; compounds in the development phase (I, II, III and launched), as indexed in MDDR; and compounds indexed in medicinal chemistry journals, categorized according to their biological activity. Comparing the distribution of the above properties, the 62 lead structures show the lowest median with respect to MW (smaller) and LogP (less hydrophobic), and the highest median with respect to LogSw (more soluble). By contrast, over 50% of the medicinal chemistry compounds with activities above 1 nanomolar have MW > 425, LogP > 4.25 and LogSw < -4.75, indicating that the reported active compounds are larger, more hydrophobic and less soluble when compared to time-tested quality leads. In the MDDR set, a progressive constraint to reduce MW and LogP, and to increase LogSw, can be observed when examining trends in the developmental sequence: phase I, II, III and launched drugs. These trends indicate that other properties besides binding affinity, e.g., solubility and hydrophobicity, need to be considered when choosing the appropriate leads.

Entities:  

Mesh:

Year:  2002        PMID: 12489682     DOI: 10.1023/a:1020877402759

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  13 in total

1.  Parallel personal comments on "classical" papers in combinatorial chemistry.

Authors:  M Lebl
Journal:  J Comb Chem       Date:  1999-01

2.  The Design of Leadlike Combinatorial Libraries.

Authors: 
Journal:  Angew Chem Int Ed Engl       Date:  1999-12-16       Impact factor: 15.336

3.  Innovation in the pharmaceutical industry.

Authors:  D F Horrobin
Journal:  J R Soc Med       Date:  2000-07       Impact factor: 5.344

Review 4.  Cheminformatics: a tool for decision-makers in drug discovery.

Authors:  T Olsson; T I Oprea
Journal:  Curr Opin Drug Discov Devel       Date:  2001-05

5.  Is there a difference between leads and drugs? A historical perspective.

Authors:  T I Oprea; A M Davis; S J Teague; P D Leeson
Journal:  J Chem Inf Comput Sci       Date:  2001 Sep-Oct

6.  Diverse viewpoints on computational aspects of molecular diversity.

Authors:  Y C Martin
Journal:  J Comb Chem       Date:  2001 May-Jun

Review 7.  Chemical space navigation in lead discovery.

Authors:  Tudor I Oprea
Journal:  Curr Opin Chem Biol       Date:  2002-06       Impact factor: 8.822

8.  High Throughput Screening for Drug Discovery: Continually Transitioning into New Technology.

Authors: 
Journal:  J Biomol Screen       Date:  1999

9.  Serendipity and structured research in drug discovery.

Authors:  G de Stevens
Journal:  Prog Drug Res       Date:  1986

10.  Atom/fragment contribution method for estimating octanol-water partition coefficients.

Authors:  W M Meylan; P H Howard
Journal:  J Pharm Sci       Date:  1995-01       Impact factor: 3.534

View more
  27 in total

1.  Can we really do computer-aided drug design?

Authors:  Matthew Segall
Journal:  J Comput Aided Mol Des       Date:  2011-12-11       Impact factor: 3.686

2.  Incorporation of in silico biodegradability screening in early drug development--a feasible approach?

Authors:  Thomas Steger-Hartmann; Reinhard Länge; Klaus Heuck
Journal:  Environ Sci Pollut Res Int       Date:  2010-10-28       Impact factor: 4.223

3.  ZINC--a free database of commercially available compounds for virtual screening.

Authors:  John J Irwin; Brian K Shoichet
Journal:  J Chem Inf Model       Date:  2005 Jan-Feb       Impact factor: 4.956

4.  Development and validation of a modular, extensible docking program: DOCK 5.

Authors:  Demetri T Moustakas; P Therese Lang; Scott Pegg; Eric Pettersen; Irwin D Kuntz; Natasja Brooijmans; Robert C Rizzo
Journal:  J Comput Aided Mol Des       Date:  2006-12-06       Impact factor: 3.686

5.  Managing, profiling and analyzing a library of 2.6 million compounds gathered from 32 chemical providers.

Authors:  Aurélien Monge; Alban Arrault; Christophe Marot; Luc Morin-Allory
Journal:  Mol Divers       Date:  2006-09-21       Impact factor: 2.943

6.  Leadlikeness and structural diversity of synthetic screening libraries.

Authors:  Herman J Verheij
Journal:  Mol Divers       Date:  2006-09-21       Impact factor: 2.943

7.  Lead-like, drug-like or "Pub-like": how different are they?

Authors:  Tudor I Oprea; Tharun Kumar Allu; Dan C Fara; Ramona F Rad; Lili Ostopovici; Cristian G Bologa
Journal:  J Comput Aided Mol Des       Date:  2007-02-28       Impact factor: 3.686

8.  Throughput and efficiency of a mass spectrometry-based screening assay for protein-ligand binding detection.

Authors:  Erin D Hopper; Petra L Roulhac; Michael J Campa; Edward F Patz; Michael C Fitzgerald
Journal:  J Am Soc Mass Spectrom       Date:  2008-06-27       Impact factor: 3.109

9.  How "drug-like" are naturally occurring anti-cancer compounds?

Authors:  Fidele Ntie-Kang; Lydia L Lifongo; Philip N Judson; Wolfgang Sippl; Simon M N Efange
Journal:  J Mol Model       Date:  2014-01-24       Impact factor: 1.810

10.  BDDCS applied to over 900 drugs.

Authors:  Leslie Z Benet; Fabio Broccatelli; Tudor I Oprea
Journal:  AAPS J       Date:  2011-08-05       Impact factor: 4.009

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.