Lead optimization is a complex and iterative process for identifying an improved drug lead with the goal of progressing to a preclinical candidate by altering the chemical structure of a confirmed hit. A diversity of properties such as target’s specificity, selectivity, pharmacodynamics, pharmacokinetics and toxicological properties are improved by modifying the chemical structures of compounds or biologics in lead optimization process. Alfa Chemistry determines accurately the basic characteristics of leads to understand the need for lead optimization. We fully consider basic attributes: potency, bioavailability, duration, safety, and pharmaceutical acceptability to ensure a program's ultimate success.
Figure 1. New drug discovery process. (Li, Q.; Kang, C. B. 2021)
Advantages of Lead Optimization
- Improve probability of success
- Reduce costs and cycle time
- Earlier, better development decisions
- Opportunities to translate findings from 'bench to bedside'
In order to provide you with better quality leads that have a high probability of success in clinical development. Alfa Chemistry has developed the following mentioned lead optimization approaches:
- In vitro ADMET
Early determination of the absorption, distribution, metabolism, excretion (ADME), and toxicological (Tox) properties of the lead series can be useful in balancing the risk-reward ratio and improve project productivity. We mainly apply QM/MM method, molecular dynamics simulation, machine learning method and density functional theory (DFT) calculation for ADMET analysis.
1. We have established a hybrid QM/MM method to simulate the basic biological process by building high-quality protein models.
2. Our molecular dynamics simulation can be used to explore the conformational energy that protein molecules can obtain using free energy perturbation (FEP) method to calculate ΔGbind.
3. Alfa Chemistry provides various 'simple' linear models such as those used in Free-Wilson and Hammett analyses. In addition, some nonlinear models are also available to capture more complex relationships between structure and activity.
4. We are capable of employing multiple theory models to determine the pharmacokinetic and pharmacodynamic properties.
- In silico and PBPK modelling
Our PBPK modelling technique allows for the data integration within the context of a virtual animal or human model. Alfa Chemistry's experienced modelling and simulation team provides guidance throughout the lifetime of your project, helping to:
1. Predict efficacious pharmacological dose through integrate PK prediction with PK/PD hypothesis.
2. Evaluate the drug-drug interaction risks.
3. Simulate absorption, distribution, metabolism and elimination in humans and animals from a range of dose routes including oral, intravenous, inhaled, ocular and dermal.
- Metabolites identification
Metabolites can be identified by our dedicated team with their skill and experience. We combine high levels of expertise with a wide range of state-of-the-art computational tools and technologies.
1. MS/MS spectrum identification of metabolites in silico simulation: we analyze the secondary mass spectrometry of known compounds, predict the secondary mass spectrum of unknown compounds, and rank the candidate structures of specific maps to identify metabolites.
2. Metabolite identification based on prediction of fragmentation method: we predict all possible fragmentation manners of the compound based on the structure of the compound, and match the ion fragment combination generated by all fragmentation methods with the actual mass spectrum spectrum to obtain the best candidate compound structure.
3. Metabolite identification based on molecular fingerprint characteristics: predict molecular fingerprint characteristics based on the fragment map of the compound, and then search the structural database based on these characteristics to obtain candidate compounds.
4. Fragmentation trees (FTs): we apply FTs in the de novo identification of molecular formulas of unknown compounds and database search for structurally similar compounds.
5. Metabolite identification based on prediction of metabolic reaction: we predict metabolites based on the spatial structure of precursor compounds, the strength of chemical bonds, and common metabolic pathways.
- Physicochemical analysis
Our computational chemistry group can predict physicochemical properties which play important roles in the design of appropriate hit expansion libraries and lead optimization. We provide physicochemical analysis of compounds from different chemical series to identify hit clusters with properties to help guide series selection. At Alfa Chemistry, we offer state-of-the-art determination of the following physicochemical parameters: Lipophilicity and aqueous solubility.
Features of Our Optimized Leads
- High potency: produce a desirable pharmacological response
- Bioactivity: enable to reach the target
- Duration: remain in circulation for sufficient time
- Safety: have sufficient selectivity for the targeted response relative to non-targeted responses
- Pharmaceutical acceptability: have suitable pharmaceutical properties such as reasonable synthetic pathway, adequate aqueous solubility, satisfactory dissolution rate, good chemical stability, and others.
Our lead optimization services remarkably reduce the cost, promote further experiments, and accelerate the process of drug design for customers worldwide. Our personalized and all-around services will satisfy your innovative study demands. If you are interested in our services, please don't hesitate to contact us. We are glad to cooperate with you and witness your success!
- Li, Q.; Kang, C. B. Mechanisms of Action for Small Molecules Revealed by Structural Biology in Drug Discovery. International Journal of Molecular Sciences. 2020, 21(15): 5262.