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Hit-to-lead (H2L) Optimization

Definition
Definition
Definition

Hit-to-Lead (H2L) optimization is a critical phase in the drug discovery process where initial "hit" compounds, identified through high-throughput screening or other methods, are further refined and optimized to improve their drug-like properties. The goal of H2L optimization is to enhance the potency, selectivity, pharmacokinetics, and safety profile of the hit compounds, transforming them into viable lead compounds suitable for further preclinical development.

Importance in Computational Drug Discovery

  1. Efficiency: Computational tools can rapidly analyze and optimize hit compounds, significantly reducing the time and cost associated with experimental approaches.
  2. Predictive Modeling: In silico methods can predict key properties such as binding affinity, ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles, and off-target effects, guiding the optimization process.
  3. Structure-Activity Relationship (SAR): Computational techniques can elucidate SARs, helping researchers understand which structural modifications will likely improve the desired properties.
  4. Virtual Screening: Computational tools can screen large libraries of analogs or derivatives of hit compounds, identifying those with the most promising profiles for further optimization.
  5. Simulation and Analysis: Molecular dynamics simulations and energy calculations can provide insights into the binding mechanisms and stability of hit compounds, informing rational design decisions.

Key Tools

  1. Schrödinger Suite: A comprehensive software package offering tools for molecular modeling, docking, virtual screening, and ADMET predictions, widely used in H2L optimization.
  2. MOE (Molecular Operating Environment): Provides tools for structure-based design, molecular simulations, and SAR analysis, aiding in the optimization of hit compounds.
  3. KNIME: An open-source platform for data analytics with various cheminformatics and computational chemistry plugins, useful for virtual screening and SAR analysis.
  4. RDKit: An open-source cheminformatics software that provides functionalities for molecular modeling, virtual screening, and predictive modeling.
  5. GROMACS: An open-source molecular dynamics package that can be used to simulate the behavior of hit compounds and their interactions with targets.
  6. Deep Origin Tools: Balto supports hit-to-lead optimization by enabling rapid in silico profiling of molecular properties, structure-based docking, and toxicity prediction to prioritize and refine chemical series for drug development.

Literature

"Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery"

  • Publication Date: N/A
  • DOI: 10.1007/978-1-4939-7465-8_19
  • Summary: This paper provides a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in H2L and lead optimization stages of structure-based drug discovery.

"Chemistry-driven Hit-to-lead Optimization Guided by Structure-based Approaches"

  • Publication Date: 2018-07-27
  • DOI: 10.1002/minf.201800059
  • Summary: Focuses on chemistry-driven and structure-based computational methods for H2L optimization, emphasizing a strategy developed in the laboratory to tackle the difficult problem of H2L optimization.

"Integrated Strategy for Lead Optimization Based on Fragment Growing: The Diversity-Oriented-Target-Focused-Synthesis Approach"

  • Publication Date: 2018-06-08
  • DOI: 10.1021/acs.jmedchem.8b00653
  • Summary: Reports a time- and cost-efficient integrated strategy for H2L optimization, combining focused-chemical-library design, virtual screening, robotic diversity-oriented de novo synthesis, and automated in vitro evaluation.

"In silico tools used for compound selection during target-based drug discovery and development"

  • Publication Date: 2015-07-21
  • DOI: 10.1517/17460441.2015.1043885
  • Summary: Reviews the impact of in silico tools on various stage gates of target-based drug discovery, suggesting further development of multiparameter models and integration of biologists, medicinal chemists, and computational chemists into one team.

"Discovery and optimizing polycyclic pyridone compounds as anti-HBV agents"

  • Publication Date: 2020-08-04
  • DOI: 10.1080/13543776.2020.1801641
  • Summary: Provides an overview of polycyclic pyridone-related anti-HBV agents, discussing high-throughput screening (HTS), H2L optimization, and other strategies in the discovery and development of HBV inhibitors.