Screening Strategies Used in Drug Discovery
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Though rewarding, the process of drug discovery is a complicated journey filled with uncertainties. The process begins with the identification of a disease or a therapeutic area with an unmet need. Once a “druggable” target is found, the process of drug screening starts. In drug screening, molecules that can interact with the target and/or facilitate the desired phenotypic response are identified.
Advances in combinatorial chemistry and molecular biology have facilitated the identification of an increased number of molecular targets and this has demanded the development of novel screening approaches.
This article highlights some of the different screening strategies used during drug discovery. Key advances and challenges associated with the techniques are also discussed.
High-throughput screening in drug discovery
High-throughput screening (HTS) is a process used routinely in early-stage drug discovery. HTS is used to detect “hit” molecules with activity against a target of interest, from large compound libraries that can comprise thousands of molecules. Once identified, these hit molecules are validated and refined to produce lead compounds with improved selectivity and potency which can be further tested to identify a potential drug candidate for preclinical testing. HTS involves the use of robotics, liquid/ microplate handling systems and microplate readers to detect, track and quantify the events. It also requires specialized software for instrumentation control and data processing.
Though a key tool, assessing drug properties such as toxicity and bioavailability may be a challenge with HTS. HTS is basically used to assist lead optimization – it may be seen as a fast scan of biological entities in which candidates with poor or negligible effect can be quickly excluded.
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High-content screening – Taking high-throughput screening to the next level
High-content screening (HCS), a technique originally developed to complement HTS, has gained immense popularity in recent years. HCS integrates the efficiency of high-throughput techniques with cellular imaging to collect quantitative data from complex biological systems.
“High-throughput assays using multititre plates and automated fluorescence microscopy converged in the 1990s when the term "high-content screening" was coined to emphasize the complex subcellular morphological and intensity-based readouts that allow studying variations in a cell population compared to a single population-averaged readout per perturbation,” explains Janos Kriston-Vizi, group leader of the Bioinformatics Image Core (BIONIC) at the Laboratory for Molecular Cell Biology (LMCB), University College London.
With HCS, multiple properties of individual cells or organisms can be studied at once. Automated microscopy, image processing and visualization tools are used in combination to extract data from cell populations. HCS typically involves fluorescence imaging of samples in a high-throughput format and provides quantitative reports on various specifics such as the spatial distribution of targets and individual cell and organelle morphology.
Kriston-Vizi says, “High-content screening improves the throughput in preclinical drug discovery. Screening small molecule, natural product, genetic or approved drug libraries using a monolayer cell culture format allows you to test thousands of perturbations in a single experiment while preserving physiological relevance compared to biochemical assays.”
Most HCS is currently carried out using conventional or two-dimensional (2D) tissue culture. However, 3D cell culture models are also being explored. By increasing physiological relevance, 3D models are set to transform the HCS arena. “3D high-content screening with spheroids and organoids increases the physiological relevance and aims to reduce the high attrition rate in drug discovery”, notes Kriston-Vizi.
Fragment-based drug discovery – The bottom-up approach
Fragment-based drug discovery (FBDD) is another well-known strategy used for drug discovery. In contrast to HTS campaigns, whereby huge libraries of complex compounds are screened, FBDD uses smaller libraries containing hundreds of low-complexity compounds or “fragments”. Compared to HTS, FBDD requires lower research investments.
By using fragments, the complexity of the compounds being screened is reduced, allowing for more of the target’s binding site to be investigated (Figure 1). FBDD also acts as a great starting point to design lead compounds with greater ligand efficiency. FBDD thus allows a bottom-up approach through which a wider space can be investigated, which enables the development of higher affinity lead compounds with high specificity.
Figure 1: Fragments are screened against a target of interest to identify hits that can later be expanded to produce larger molecules. Credit: Technology Networks.
In FBDD, screening is done using a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance and thermophoresis. The next step is the structural characterization of fragment binding using nuclear magnetic resonance spectroscopy or X-ray crystallography. The latest innovation in the FBDD workflow is a high-throughput technique whereby individually soaked fragments are screened in parallel, using X-ray crystallography.
The use of artificial intelligence such as deep learning is anticipated to accelerate the optimization of fragment hits to produce lead compounds. With artificial intelligence, optimization of fragment hits can be achieved while considering important factors such as the ADMET properties, solubility parameters, biological activity and synthetic feasibility.
Virtual screening – Exploring the in silico technique
Virtual screening (VS) is an in silico technique used for the identification of bioactive drug candidates. VS strategies involve the use of computational methods to automatically screen huge databases of known 3D structures.
Javier Vázquez Lozano, Ph.D., adjunct lecturer, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB) and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona explains, “The use of VS tools mainly encompasses two approaches: one based on protein–ligand interactions and the other based on the molecular similarity principle. For their applications, only a reference starting point (the protein structure of the receptor [or target] of interest for the former and at least one known active compound for the latter) and a database of virtual compounds are required. This makes them flawless tools for the initial stages of drug discovery where the data of the system is reduced. With the information provided by the protein receptor or a known compound, we can search a public or private database, using a commercial VS tool, for a new potential hit.”
VS techniques are reported to be an excellent option compared to HTS – the probability of finding the best result from a large virtual database is also high with VS. In addition, as VS is a computer-based screening approach it is considered a cost-effective means of identifying compounds, compared to “physical” approaches where there is a need to screen vast libraries. VS assists in identifying the most promising hits able to bind to the target and only the most promising molecules are synthesized. Additionally, VS can be explored to identify toxic compounds or those with unfavorable pharmacodynamic and pharmacokinetic properties.
The number of new techniques and software that can be applied in this strategy has grown considerably in recent years. Lozano explains, “The most remarkable advances in this field are linked to advances in technology. Among the most notable innovation, I would highlight the methods that combine structure – and ligand-based approaches. As result, the search process is enriched with an acceptable computational cost. Furthermore, with the booming era of big data, the merging of machine learning methods with traditional VS strategies should be appointed. In many cases, they have shown an efficient way for drug designers to deal with important biological properties from many compound databases. However, their results still need to be analyzed with caution.”
Which screening strategy to choose?
VS is less expensive and time-consuming than conventional HTS, but it demands meticulous implementation of each step – from target preparation to hit identification and lead optimization. With HTS, there is a high probability of off-target activity. Compared with HTS, FBDD relies on a small set of compounds to be screened. Despite lower initial potency, the fragment-based method is more efficient and offers rewarding optimization campaigns. Comparing the overall cost of the three strategies, HTS is the most expensive, requiring extensive resources.
Choosing the apt screening strategy largely depends on the drug target’s characteristics. The past success of a specific screening strategy and the expertise within an organization are also important points to consider.
