Since the 1980’s, improvements in screening technologies have resulted in throughputs that have increased from 10,000 assays per year to current levels, which can approach ultra-high-throughput screening levels of more than 100,000 assays per day. It has been predicted that the number of compounds tested per week will go from 1 million in 2001 to 7 million in 2004. Consequently, high-throughput screening (HTS) has become a cornerstone in the drug discovery process. A screen of 100,000-300,000 compounds produces approximately 100-300 hits when defined as molecules that produce “positive HTS results”. On average, only one or two of these become lead compound series. Larger screens of up to 1,000,000 compounds lasting several months may be required to generate something closer to five leads. Lead discovery is therefore a high-risk endeavor. Thus, although chemistry and screening throughputs have massively increased over the past decade, lead discovery productivity has not necessarily increased accordingly. This inability to identify multiple high-quality leads that are novel, tractable, and efficiently optimizable remains a key bottleneck in today’s drug discovery environment.

Although many people in the industry have focused on the drive for higher throughput, identifying the characteristics of good leads is perhaps more important. In particular appreciating the concept that druglikeness (ie the nature of a therapeutic candidate) differs from leadlikeness (ie the nature of a good lead) is of primary importance. The science of leadlikeness is therefore becoming a key element of drug discovery.

Over the past decade, the industry has been active in defining druglike properties. The much-cited “Lipinski rule of five” derives empirically from the vast amount of data that the industry has gathered on properties that maximize an oral drug candidate’s probability of surviving development: molecular weight (MW) < 500, number of hydrogen bond donors < 5, number of hydrogen bond acceptors < 10, and ClogP < 5.

On the other hand field leaders such as Gilbert M. Rishton from Amgen have been defining leadlike properties. In general, undesirable leads include protein-reactive molecules that form covalent bonds in vitro. Such leads, which include beta lactam-like antibiotics and alkylating anti-neoplastic agents, can be adequately identified using functional biological assays however the frequency of false positive is high when using HTS-compatible biochemical assays. Likewise, electrophiles and warhead-containing agents such as chelators and polyionics are also prone to being false positives. The frequent hitter concept has also emerged and describes molecules that are often identified as hits in multiple assay types. This artifactual behavior may be related to non-specificity or assay interference (eg auto-fluorescence). Although each of these types of molecule could potentially produce therapeutic candidates, relying on HTS technology for their identification is unlikely to be successful.

In the context of modern day HTS technology desirable leads generally display non-covalent high affinity binding to their target; reversible, time-independent competitive binding; and tractability in SAR studies. These characteristics are related to physiochemical properties. On average, historical leads have been characterized by lower MW and lipophilicity (ClogP), fewer aromatic rings and hydrogen bond acceptors, and lower Andrew's binding energy functions than their corresponding final drugs. In general libraries consisting of compounds with MW = 100–350 and ClogP = 1–3 are superior for finding leads than those comprising druglike compounds, with higher MW and ClogP. The MW and lipophilicity of initial leads typically increase during the lead optimization process. Thus, if the initial lead is already too druglike, then the optimization process that is likely to be needed to tailor the molecule to the new receptor or enzyme will likely result in a higher MW and a more lipophilic drug candidate. The candidate may thus no longer possess druglike properties. This suggests that when looking for leads, the guidelines given by Lipinski should be lowered so that leads that are found by HTS give more “room” for further property optimization.

With rapid improvements in assay technology it is easy to overly focus on the quantity of molecules that can be screened rather than the quality of the library being assayed. Reversing this trend will ultimately improve the efficiency of drug discovery and bearing in mind the difference between leadlikeness and druglikeness will contribute greatly to meeting this goal and reducing the high rate of program attrition in the pharmaceutical industry.

Entry date Wednesday, July 09, 2003

Adapted from Rishton, Drug Discov Today. 2003 Jan 15;8(2):86-96.

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