Hit to lead

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Hit to lead (H2L) also known as lead generation is a stage in early drug discovery where small molecule hits from a high throughput screen (HTS) are evaluated and undergo limited optimization to identify promising lead compounds.[1] These lead compounds undergo more extensive optimization in a subsequent step of drug discovery called lead optimization (LO).[2][3] The drug discovery process generally follows the following path that includes a hit to lead stage:

The hit to lead stage starts with confirmation and evaluation of the initial screening hits and is followed by synthesis of analogs (hit expansion). Typically the initial screening hits display binding affinities for their biological target in the micromolar (10−6 molar concentration) range. Through limited H2L optimization, the affinities of the hits are often improved by several orders of magnitude to the nanomolar (10−9 M) range. The hits also undergo limited optimization to improve metabolic half life so that the compounds can be tested in animal models of disease and also to improve selectivity against other biological targets binding that may result in undesirable side effects.

Hit confirmation[edit]

After hits are identified from a high throughput screen, the hits are confirmed and evaluated using the following methods:

  • Re-testing: compounds that were found active against the selected target are re-tested using the same assay conditions used during the HTS.
  • Dose response curve generation: several compound concentrations are tested using the same assay, an IC50 or EC50 value is then generated.
  • Orthogonal testing: Confirmed hits are assayed using a different assay which is usually closer to the target physiological condition or using a different technology.
  • Secondary screening: Confirmed hits are tested in a functional assay or in a cellular environment. Membrane permeability is usually a critical parameter.
  • Chemical amenability: Medicinal chemists evaluate compounds according to their synthesis feasibility and other parameters such as up-scaling or costs
  • Biophysical testing: Nuclear magnetic resonance (NMR), Isothermal Titration Calorimetry, dynamic light scattering, surface plasmon resonance, dual polarisation interferometry, microscale thermophoresis (MST) are commonly used to assess whether the compound binds effectively to the target, the stoïchiometry of binding, any associated conformational change and to identify promiscuous inhibitors.
  • Hit ranking and clustering: Confirmed hit compounds are then ranked according to the various hit confirmation experiments.
  • Freedom to operate evaluation: Hit compound structures are quickly checked in specialized databases to determine if they are patentable[4]

Hit expansion[edit]

Following hit confirmation, several compound clusters will be chosen according to their characteristics in the previously defined tests. An Ideal compound cluster will:

  • have compound members that exhibit a high affinity towards the target (less than 1 µM)
  • Moderate molecular weight and lipophilicity (usually measured as cLogP). Affinity, molecular weight and lipophilicity can be linked in single parameter such as ligand efficiency and lipophilic efficiency to assess druglikeness
  • show chemical tractability
  • be free of Intellectual property
  • not interfere with the P450 enzymes nor with the P-glycoproteins
  • not bind to human serum albumin
  • be soluble in water (above 100 µM)
  • be stable
  • have a good druglikeness
  • exhibit cell membrane permeability
  • show significant biological activity in a cellular assay
  • not exhibit cytotoxicity
  • not be metabolized rapidly
  • show selectivity versus other related targets

The project team will usually select between three and six compound series to be further explored. The next step will allow the testing of analogous compounds to define Quantitative structure-activity relationship (QSAR). Analogs can be quickly selected from an internal library or purchased from commercially available sources. Medicinal chemists will also start synthesizing related compounds using different methods such as combinatorial chemistry, high-throughput chemistry or more classical organic chemistry synthesis.

Lead optimization phase[edit]

The objective of this drug discovery phase is to synthesize lead compounds, new analogs with improved potency, reduced off-target activities, and physiochemical/metabolic properties suggestive of reasonable in vivo pharmacokinetics. This optimization is accomplished through chemical modification of the hit structure, with modifications chosen by employing knowledge of the structure-activity relationship (SAR) as well as structure-based design if structural information about the target is available.

Lead optimization is concerned with experimental testing and confirmation of the compound based on animal efficacy models and ADMET (in vitro and in situ) tools.

Application of ADME-Tox tools has increased the success rate of drug development as well as helped in reducing the cost and time factors. The use of in silico and in vitro ADME-Tox has found universal acceptance. In silico tools provide a much higher throughput, but they suffer from adequate predictability, which limits their use. However, the reliance on in silico models is due to their ability to predict which compounds should be synthesized based on confirmed hits and structural modifications since it helps in selecting a drug-like compound. The best way to implement the ADME-Tox property prediction is the integration of both in silico and in vitro approaches to supplement each other for the production of candidate drugs.

See also[edit]


  1. ^ Deprez-Poulain R, Deprez B (2004). "Facts, figures and trends in lead generation". Curr Top Med Chem 4 (6): 569–80. doi:10.2174/1568026043451168. PMID 14965294. 
  2. ^ Keserű GM, Makara GM (August 2006). "Hit discovery and hit-to-lead approaches". Drug Discov. Today 11 (15-16): 741–8. doi:10.1016/j.drudis.2006.06.016. PMID 16846802. 
  3. ^ Bleicher KH, Böhm HJ, Müller K, Alanine AI (May 2003). "Hit and lead generation: beyond high-throughput screening". Nat Rev Drug Discov 2 (5): 369–78. doi:10.1038/nrd1086. PMID 12750740. 
  4. ^ Cockbain J (2007). "Intellectual property rights and patents". In Triggle JB, Taylor DJ. Comprehensive Medicinal Chemistry 1 (2nd ed.). Amsterdam: Elsevier. pp. 779–815. doi:10.1016/B0-08-045044-X/00031-6. ISBN 978-0-08-045044-5.