Leads identification

With nearly 20 years of experience on plant-derived natural products, NPRL has identified numerous active anti-HIV compounds including polycyclic diones, saponins, alkaloids, triterpenes, polyphenols, flavonoids and coumarins. Triterpenes have diverse structures and pharmacological activities. 

Example of the bioassay-derived fractionation and isolation of anti-HIV compounds from a typical high priority plant of interest. 

Rational Drug Design-Based Analog Synthesis 

Several naturally occurring triterpenes have been reported to show anti-HIV activity, e.g. betulinic acid, platanic acid, glycyrrhizin, mimusopic acid, gano-deriol and geumonoid.16 20 These active compounds hold the potential to serve as hits or leads for anti-HIV drug development.21,22 The focus of this chapter is bevirimat, the first in a new class of compounds termed HIV maturation inhibitors (Mis). Our discussion covers modification, structure-activity relationship (SAR), mechanism of action studies and clinical trials of bevirimat. 

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Honma T. Recent advances in de novo design strategy for practical lead identification. Med Res Rev 2003 23 606-32. 

Klebe G. Lead identification in post-genomics computers as a complementary alternative. Drug Discov Today Technol 2004 1 225-30. 

Langer T, Eder M, Hoffmann RD, Chiba P and Ecker GF. Lead identification for modulators of multidrug resistance based on in silico screening with a phar-macophoric feature model. Arch Pharm (Weinheim) 2004 337 317-27. 

In the realm of drug discovery, the detection and characterization of target-ligand interactions is a critical process for lead identification, validation, and optimization. 

Abstract High quality leads provide the foundation for the discovery of successful clinical development candidates, and therefore the identification of leads is an essential part of drug discovery. Many factors contribute to the quality of a lead, including biological, physicochemical, ADME, and PK parameters. The identification of high quality leads, which are needed for successful lead optimization, requires the optimization of all of these parameters. Parallel optimization of all parameters is the most efficient way to achieve the goal of lead identification. 

At the outset of a lead identification effort, it is imperative to establish specific criteria for potency, selectivity, ADME properties, etc. to generate a desired lead profile. This profile serves to guide the lead identification efforts based on the initial characterization of the hits. 

As mentioned in Sect. 3, it is important to establish a detailed lead profile at the beginning of a lead identification effort. Criteria vary in different lead identification or hit-to-lead groups, but generally include some or all of the following potency, functional activity, selectivity, MW, clogP, solubility, permeability, microsomal stability and/or hepatocyte clearance, and preliminary PK including oral bioavail-ability. An example of a lead profile for a kinase inhibitor project is illustrated in Table 1 [21],... 

Drug candidates that are intended for oral dosing need to have good ADME properties so that they can be dosed once or twice daily. The drug should be well absorbed, survive first pass metabolism, and have sufficiently low clearance. At the lead identification stage, the primary in vitro ADME assays employed are those that assess permeability and metabolic stability. There are a variety of assays available for both parameters, as described in the previous chapter. 

The following examples taken from the literature since 2001 are intended to illustrate the parallel optimization of some or all of the parameters discussed above, and to illustrate how some of the tools described above can aid in lead generation. It is not meant to be an exhaustive survey of the literature. The specific criteria used to define a successful lead identification campaign vary by group, as do the processes used to reach the lead stage. However, there are also many themes that are common to many of the examples below. A summary of the examples described below is contained in Table 2. 

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