High throughput screening Flashcards
Understand the importance of high throughput screening in hit
compound generation
HTS involves testing a large number of compounds in a rapid and automated manner against a specific biological target or assay. The goal is to identify compounds that interact with the target in a specific way, leading to a desirable biological effect.
The importance of HTS in hit compound generation can be summarized as follows:
Efficiency: HTS allows researchers to rapidly screen large libraries of compounds to identify those with potential therapeutic activity, significantly reducing the time and cost of the drug discovery process.
Increased hit rates: HTS can identify many potential drug candidates in a single screening, increasing the chances of finding a promising lead compound.
Identification of new chemical scaffolds: HTS can identify compounds that interact with a biological target in a novel way, leading to the discovery of new chemical scaffolds that can be used as starting points for drug development.
Optimization of lead compounds: HTS can be used to optimize lead compounds by identifying compounds with improved potency, selectivity, and pharmacological properties.
Explain the high throughput screening methodologies.
involves using microplates, typically 96 or 384 wells, to screen large numbers of compounds simultaneously. Microplate-based assays can be used to screen for a wide range of activities, such as enzyme activity, receptor binding, and cell viability.
Fluorescence-based assays: This methodology uses fluorescent dyes or proteins to detect changes in a sample’s properties. Fluorescence-based assays are particularly useful for screening for changes in protein activity, protein-protein interactions, and cellular signaling pathways.
Cell-based assays: These assays use living cells to screen for drug activity. They can be used to test the effect of compounds on a variety of cell types and processes, such as cell proliferation, apoptosis, and migration.
Label-free assays: This methodology uses sensors to detect changes in a sample’s properties without the need for labels or dyes. Label-free assays can be used to screen for a wide range of activities, such as enzyme activity, receptor binding, and cell adhesion.
Mass spectrometry-based assays: This methodology uses mass spectrometry to detect changes in a sample’s properties. It is particularly useful for screening for changes in protein activity and protein-protein interactions.
Explain the key considerations and success criteria for a successful HTS campaign.
Quality and relevance of the assay: The success of an HTS campaign largely depends on the quality and relevance of the assay used. The assay should be well-characterized and validated, and should accurately reflect the biological process or target of interest.
Compound library: The compound library used in an HTS campaign should be diverse, high-quality, and relevant to the target of interest. The size of the library should be appropriate for the assay throughput and the resources available for follow-up testing.
Automation and robotics: HTS campaigns require significant automation and robotics to enable rapid and efficient testing of large numbers of compounds. The equipment and software used should be reliable, efficient, and easy to use.
Data management and analysis: Data management and analysis are critical components of an HTS campaign. The data generated should be well-organized and easily accessible, and the software used for data analysis should be reliable and efficient.
Follow-up testing: The compounds identified in an HTS campaign should be thoroughly characterized through follow-up testing, such as dose-response assays, secondary assays, and in vivo testing. This is essential to confirm the activity and specificity of the identified compounds, and to identify potential off-target effects or toxicity.
Project timeline and budget: A successful HTS campaign requires careful planning and management of the project timeline and budget. The timeline should allow for adequate assay development, compound screening, and follow-up testing, while the budget should cover the costs of reagents, equipment, personnel, and follow-up testing.
Apply the principles and methods of HTS in identification of hit
compounds using appropriate case studies.
The SARS-CoV-2 virus, responsible for the COVID-19 pandemic, requires the protease enzyme for its replication. Thus, the protease has emerged as a potential target for antiviral drug discovery. An HTS campaign was performed by the Scripps Research Institute to identify inhibitors of the SARS-CoV-2 protease. The researchers screened over 11,000 compounds using a fluorescence-based assay that measures protease activity. They identified two compounds, PF-00835231 and PF-07304814, that showed potent inhibition of the protease with IC50 values of 5.0 μM and 0.7 μM, respectively. These compounds were then further characterized using X-ray crystallography, which revealed their binding mode to the protease active site. These hits have since been developed into clinical candidates for the treatment of COVID-19.
- Learn how new techniques are improving the success rates of HTS.
Phenotypic screening
Phenotypic screening is an approach that involves the screening of compounds for their ability to modulate a specific phenotype or cellular function, rather than a single target. This approach can identify compounds that act through multiple targets or pathways, allowing for the discovery of new mechanisms of action. Phenotypic screening can also reduce the risk of false positives and false negatives, as it considers the complexity of the cellular environment.
Fragment-based screening
Fragment-based screening is a technique that involves the screening of a library of small, low-molecular-weight compounds (fragments) for their ability to bind to a target of interest. This approach can identify fragments that bind to different regions of the target, which can then be combined to form larger, more potent compounds with increased selectivity and efficacy.
Virtual screening
Virtual screening is a computational approach that involves the use of computer algorithms to predict the binding of small molecules to a target of interest. This approach can be used to screen large compound libraries in silico, reducing the cost and time associated with experimental screening. Virtual screening can also be used to identify compounds that may not have been included in the initial screening library.
Microfluidics-based screening
Microfluidics-based screening is a technique that involves the use of microfluidic devices to miniaturize the screening process. This approach can reduce the cost and time associated with screening, as well as reduce the amount of reagents and compounds required. Microfluidics-based screening can also enable the screening of rare or precious compounds, as only small amounts are required.
Learn different quality control parameters that are routinely
employed to validate high throughput screening.
Z-factor
The Z-factor is a statistical parameter that measures the separation between the positive and negative controls in a screening assay. It is calculated using the formula: Z = 1 - (3 × SDp + 3 × SDn) / (|Mp - Mn|), where SDp and SDn are the standard deviations of the positive and negative controls, respectively, and Mp and Mn are the means of the positive and negative controls, respectively. A Z-factor value of 0.5 or greater is generally considered acceptable for HTS assays.
Signal-to-background ratio
The signal-to-background ratio is a parameter that measures the ratio of the signal from the positive control to the background signal from the negative control. A high signal-to-background ratio indicates a higher degree of separation between the positive and negative controls, which increases the reliability and accuracy of the assay.
Signal-to-noise ratio
The signal-to-noise ratio is a parameter that measures the ratio of the signal from the positive control to the noise level of the assay. A high signal-to-noise ratio indicates a low level of background noise and a high degree of sensitivity in the assay.
Coefficient of variation
The coefficient of variation (CV) is a statistical parameter that measures the variability of the assay results. It is calculated by dividing the standard deviation by the mean and expressing the result as a percentage. A low CV indicates a high degree of reproducibility and precision in the assay.
Reproducibility
Reproducibility is a measure of the ability of the assay to produce consistent results over time and across different experiments. Reproducibility can be assessed by performing replicate experiments and calculating the correlation coefficient between the results.
Robustness
Robustness is a measure of the ability of the assay to produce consistent results in the presence of minor variations in assay conditions. Robustness can be assessed by varying assay parameters such as the concentration of reagents, the temperature, or the pH, and measuring the effect on the assay results.
High throughput screening vs Computational methods
High-throughput screening involves the testing of a large number of compounds in a relatively short period of time to identify those that have a desired biological activity. This approach is typically used in the early stages of drug discovery to identify lead compounds that can be further optimized to improve their efficacy, selectivity, and safety. HTS assays can be used to identify compounds that interact with a particular target receptor or enzyme, or that have a desired cellular or physiological effect. HTS can be performed using a variety of formats, including microtiter plates, microfluidic devices, and cell-based assays.
Computational methods, on the other hand, involve the use of computer simulations and models to design and optimize drug candidates. Computational methods can be used to predict the interactions between small molecules and their target receptors or enzymes, and to optimize the chemical structure of lead compounds to improve their pharmacological properties. This approach is particularly useful for designing molecules that are specific to a particular target receptor or enzyme, and that have improved binding affinity, selectivity, and pharmacokinetic properties. Computational methods can also be used to identify new drug targets and to predict the toxicity and side effects of drug candidates.
