Drug Optimization Service
In the drug development process, lead optimization is crucial for improving the performance of hits or lead compounds and designing efficient and drug-like candidate drugs. CD ComputaBio is dedicated to providing efficient drug optimization services with its advanced computational technologies and professional R&D team, helping clients obtain superior drug candidates.
Introduction to Drug Optimization
Drug optimization is crucial in the drug development process. Lead compounds, which are chemicals or natural products with biological activity against drug targets, are the key starting point for drug optimization. The purpose of drug optimization is to enhance the selectivity of lead compounds for the selected drug target, improve their activity, and reduce side effects by optimizing the binding and non-binding interactions between the lead compound and the active site of the drug target. Computational methods play an indispensable role in the identification and optimization of lead compounds.
Fig 1. Overview of hit-to-lead optimization. (Luttens A, et al., 2022)
Advancements in Computational Drug Optimization
Computational methods are becoming increasingly important in drug optimization, with advanced scoring functions driven by artificial intelligence enhancing predictive capabilities. Despite the challenges faced by current computational technologies in simulating complex biological environments, their applications in lead compound optimization, such as molecular mechanics, thermodynamics and kinetics profiling, relative free energy calculations, and toxicology predictions, still demonstrate significant potential. Drug optimization strategies, including fragment replacement, linker design, scaffold hopping, and side chain modification, are continuously propelling drug discovery to new heights.
Fig 2. Pipeline to leverage GenAI-based lead optimization tools. (Zhang O, et al., 2024)
Our Services
CD ComputaBio utilizes computational methods to provide professional and efficient services in the lead optimization phase of drug discovery. We employ technologies such as pharmacophore studies, molecular docking, modification simulations, and QSAR to analyze the activity, selectivity, and physicochemical properties of compounds, optimize their structures, enhance efficacy, and reduce toxic side effects.
We possess profound expertise in computational chemistry, molecular biology, and drug design, enabling us to provide customized optimization strategies for various types of drug molecules. The following are our core service areas:
Small Molecule Drug Optimization
Leveraging computational simulations to optimize small molecule structure-activity relationships, regulating metabolic stability, solubility, and target selectivity, reducing off-target toxicity, and enhancing the clinical potential of anti-cancer and anti-viral drugs.
Peptide Drug Optimization
Capitalizing on machine learning and dynamic conformational analysis to design protease-resistant peptide sequences, enhancing membrane permeability and target binding affinity, thereby overcoming the bottlenecks of short half-life and low delivery efficiency in peptide drugs.
PROTAC Drug Optimization
Employing molecular dynamics simulations of PROTAC ternary complex dynamics to optimize linker length and chemical bond flexibility, balancing target protein degradation efficiency and cell permeability, thus expanding the application scope of "undruggable" targets.
Nucleic Acid Drug Optimization
Utilizing chemical modification prediction and delivery vehicle simulation to optimize the stability and immunogenicity of siRNA/mRNA, designing highly efficient targeted lipid nanoparticles (LNPs), and improving the tissue specificity and sustained efficacy of gene therapies.
Antibody Drug Optimization
Integrating AI epitope engineering and free energy calculations to optimize antibody affinity, humanization degree, and Fc effector functions, reducing the risk of cross-reactivity, and accelerating the clinical translation of antibodies for cancer and autoimmune diseases.
Protein Drug Optimization
Harnessing computational protein design and interface energy analysis to modulate protein-protein interactions, optimizing stability, half-life, and targeting specificity, and developing novel fusion proteins and engineered cytokine therapies.
Main Strategies for Drug Optimization
The optimization of drugs is necessary to enhance their efficacy and chemical accessibility, as well as to eliminate any undesirable effects on their pharmacokinetic properties, such as metabolic stability, solubility, and cell permeability. The main optimization strategies for lead compounds include:
Functional Group Modification
Modifying the natural structure of lead compounds by adding or replacing functional groups and adjusting ring systems. If the biomolecular structure is known, structure-based design is performed to further optimize the compounds and improve their targeting.
Structure-Activity Relationship (SAR) Optimization
CD ComputaBio establishes the structure-activity relationship of lead compounds, mainly addressing issues such as absorption, distribution, metabolism, excretion, and toxicity (ADMET), to enhance the activity of drug candidates while retaining the basic structural core of the lead compounds.
Pharmacophore-based Optimization
By identifying and adjusting key pharmacophore groups in drug molecules, optimizing their spatial arrangement and chemical properties, enhancing drug activity, selectivity, and pharmacokinetic characteristics, while reducing toxicity, we contribute to the development of more effective and safer drug candidates.
Scaffold Hopping-driven Optimization
By employing the scaffold hopping approach, we modify the core structure of lead compounds. This helps address the chemical accessibility issues of natural lead compounds and creates new lead compounds with unique properties.
CD ComputaBio looks forward to establishing collaboration with you in the drug optimization phase, jointly accelerating the new drug development process and improving the success rate of drug candidates. Whether you have any questions about our services or wish to explore collaboration opportunities, please feel free to contact us.
References:
- Luttens, A.; et al. Ultralarge virtual screening identifies SARS-CoV-2 main protease inhibitors with broad-spectrum activity against coronaviruses[J]. Journal of the American Chemical Society. 2022, 144(7): 2905-2920.
- Zhang, O.; et al. Deep Lead Optimization: Leveraging Generative AI for Structural Modification[J]. Journal of the American Chemical Society. 2024, 146(46): 31357-31370.
* For Research Use Only.
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