Lead compounds can be either natural or chemical products possessing biological activity against drug targets. Thus, identification and optimization of lead compounds is an important step of drug discovery. Lead optimization is the final phase of drug discovery that focuses on optimizing different characteristics of lead compounds, such as target selectivity, biological activity, potency, and toxicity potential.

To further approve the molecule as a preclinical candidate, it evaluates the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of the compound. Animal models are used to analyze the effectiveness of the lead compound in modulating the disease. These assessments are not only limited to the lead optimization phase but also extend to other drug development stages.

Modification of compounds for the purpose of improved characteristics are studied using high-throughput techniques such as magnetic resonance and mass spectrometry or computational methods. Pharmacophore studies, molecular dynamics, QSAR, and molecular docking are some computational methods used in this phase.

In this article, we will cover the methods to identify lead compounds, strategies used for lead optimization, and tools and equipment involved in the process.

Identifying Lead Compounds

Lead identification is the process of identifying chemical compounds or molecules for downstream drug discovery processes, such as lead optimization, followed by clinical development. The hits are filtered based on many physical properties, such as solubility, metabolic stability, purity, bioavailability, aggregation, and drug-related properties.

The techniques most extensively used in lead compound identification include molecular docking (MD) simulation, high-throughput screening, and virtual screening. Machine learning (ML) and deep learning (DL) at recent times play a crucial role in enhancing the efficacy and specificity of identifying potential drug candidates by systematically exploring chemical space. It offers the most accurate prediction of lead compound generation by analyzing large-scale data of lead compounds.

High-throughput screening (HTS) expedites the drug discovery process by efficiently evaluating extensive compound libraries; often, several thousand compounds are analyzed within a day or week. It employs automated robotic systems to analyze metabolic, pharmacokinetic, and toxicological data for new drugs. Further, as many as 100,000 assays can be conducted per day using the ultra-high-throughput Screening (UHTS) method. The technique can detect hits at micromolar or sub-micromolar levels for their development into a lead compound.

HTS offers significant advantages over traditional screening methods, including:

• Enhanced automated operations
• Reduced human resource requirements
• Improved sensitivity and accuracy through new assay methods
• Lower sample volumes
• Cost savings in culture media and reagents.

Lead Optimization Strategies

Lead optimization in drug discovery involves the synthesis and characterization of the identified lead compound. The compounds at the lead optimization phase are those that already met the initial requirements in early drug discovery phases and will be prepared for final characterization to be identified as preclinical drug candidates.

The selected drugs are examined for its behavior and genotoxicity using biochemical assays, such as Irwin's test and the Ames test. Further, at the end of the lead optimization stage, the lead compounds are characterized using drug-induced metabolism and metabolic profiling, high-dose pharmacology, and PK (pharmacokinetic)/PD (pharmacodynamic) studies.

The optimization of lead compounds is necessary to improve drug efficacy and chemical accessibility and eliminate any undesirable effects on their pharmacokinetic properties, such as metabolic stability, solubility, and cellular permeability.

The main optimization strategies for lead compounds include:

• Direct chemical manipulation of functional groups: In this approach, the natural structure of the lead compounds is modified by adding or swapping functional groups, making isosteric replacements, or adjusting ring systems. If the structure of the biomolecule is known, structure-based design can help optimize the compound in the target-focused approach.
• Structure-activity relationship (SAR) directed optimization: It involves making further modifications and establishing structure-activity relationships in lead compounds, following the biological information extracted from the first approach. The approach typically focuses on tackling challenges related to absorption, distribution, metabolism, excretion, and toxicity (ADMET) and improving lead candidate effectiveness. This all is done without making significant alterations to the basic structural cores of natural products.
• Pharmacophore-oriented molecular design based on the natural templates: The approach involves significant modification in the core structure of the lead compound by using modern drug design methods such as structure-based design and scaffold hopping. It addresses challenges associated with the chemical accessibility of natural leads and creates novel leads with distinctive properties.

Technology And Tools in Lead Optimization

Lead optimization techniques involve many in silico, in vitro, and in vivo approaches that have been thoroughly tested and proven reliable. The two most frequently used lead optimization methods include Nuclear Magnetic Resonance (NMR) and Mass Spectrometry methods. NMR is used to extract information on the molecular structure of lead compounds and their interaction at the atomic level. Its further applications include target druggability assessment, hit validation and optimization, pharmacophore identification, and structure-based drug design. The mass spectrometry (MS) approaches, such as LCMS, are used to characterize drug metabolism and pharmacokinetics, especially metabolite identification for lead optimization.

Computational or in silico approaches are found to be very helpful in improving the efficacy and efficiency of lead compounds in drug discovery. A few of these methods include LEADOPT and three-dimensional quantitative structure-activity relationship (3D-QSAR) methods, such as comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA).

The LEADOPT is used in the structural modification of lead compounds through fragment growing and fragment replacing operations. The alterations are specifically conducted within the active pocket of the target protein while preserving the core scaffold structure of the ligand. The bioactivity of the novel molecule generated in the ad optimization process is analyzed by studying ligand efficiency, and the oxic and pharmacokinetics properties are investigated using the SCADMET program.

Challenges and Future Perspectives

Lead identification and optimization in drug discovery and development includes processes that narrow down the lead compounds and optimize them for further development as clinical drug candidates. Even after choosing target lead compounds, only one in ten make it to the market. The financial risks of failure increase at the clinical stage, making it crucial to find ways to improve success rates. Thus, more clinical studies such as enhanced toxicology screens, biomarker identification, and predictive translational models based on a thorough disease understanding are required to be conducted. Academic-industry partnerships are seen as valuable in addressing these challenges and ultimately delivering more effective drugs to patients.

There’s a need to enhance the throughput of the drug discovery screening process and reduce the costs at the same time. This has driven the regular use of homogeneous, fluorescence-based assays in miniaturized formats.

In order to elevate the success rate of lead compounds reaching the market and boost drug production efficiency, it is crucial to enhance the efficiency of drug-discovery screening processes–all while cutting down on development and operational expenditures. The practice has driven the development and use of miniaturized form homogeneous, fluorescence-based assays. Further, the introduction of high-density plates with 384 wells; automation of dilution processes, assay functionality, and liquid handling; integration of machine learning technologies, and powerful fluorescence detection systems promise revolutionary results in drug discovery screening.

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