Making the right choices in hit identification
Like all parts of the drug discovery phases, successful hit identification is as much to do with solid decision making as it is about high-quality science.
The main focus of hit identification is to identify and validate hit series of molecules that have the best chance of being developed into drug-like compounds. But how do you go about identifying hit molecules, and what are the most effective ways for teams to take drug candidates to future hit-to-lead and lead optimisation stages?
Let’s look at the importance of quality compound libraries for successful hit identification and validation, and how multidisciplinary teams capable of using a variety of screening methods ultimately make drug discovery faster, better and more likely to succeed.
How do you identify hit molecules?
Most hit compounds are found through high-throughput screening (HTS) of diverse compound libraries. This involves two key aspects: a high-quality library, and using many different types of assays to screen candidate compounds for desired efficacy in a variety of different ways.
But what does this look like in practice?
In recent years, technological advances in assay automation have had a huge impact on HTS, allowing small teams of scientists to screen thousands of different compounds in simple in vitro tests like 3D cell imaging, droplet assays, calcium imaging functional assays, and mass spectrometric detection.
Yet HTS assays are just the beginning, with an array of counter, orthogonal, selectivity and secondary assays required to identify the best hit series. And in fact, using multiple assays is not enough.
The successful identification and expansion of hit molecules is hugely dependent on the quality and size of a compound library. For example, the latest fragment-based drug discovery (FBDD) approaches of screening fragment libraries can rapidly identify and characterise small molecule (fragment) hits.
FBDD programmes use high-throughput biophysical screening techniques to deliver small fragments that may only bind weakly to a biological target but can produce lead candidates with higher affinity by combining fragments into larger molecules. When more structural information is fed into the program, say by structural biologists and chemists, the progress can be faster.
Decisions, decisions: taking an ‘active’ to a ‘hit’
Technological advances like study automation and in silico screening methods have significantly improved the throughput and efficiency of HTS in recent years, meaning considerably more time is spent analysing datasets and making decisions about which candidates show sufficient efficacy and utility to be taken to hit-to-lead phases.
Making efficient and robust decisions when determining whether to take an ‘active’ to a ‘hit’ is ultimately down to two points in the hit identification process:
- Early assessment of the hit-finding landscape of the target, with strict selection of the optimal screening optimisation approaches that are best suited to the project.
- The decision-making process of transitioning from an ‘active’ molecule detected in screening to a robustly validated ‘hit’.
The transition from an ‘active’ to a ‘hit’ is critically important for the eventual success or failure of drug candidates. For example, it is important to validate the purity and structure, rule out any false positive compounds, and ensure that compound synthesis is optimal.
For this, carefully designed experimental programmes using combinations of multiple orthogonal biophysical and biochemical techniques are crucial, playing an important role in cross-validating results. These comprehensive study design suites are vital for improving the chances of success when moving from an ‘active’ to a ‘hit, and in building investors’ confidence, too.
Integrated approaches in hit identification
Successful hit identification and validation projects are equally dependent on comprehensive screening approaches and the ability for teams to make effective decisions about which candidates should be taken to hit-to-lead phases.
In other words, the data is only as good as the team interpreting it.
That’s why it’s important to have truly integrated teams of experts with diverse expertise who can work together on hit identification in one place.
There can be tough decisions to make about risk-benefit profiles of molecules, and the choices made at this step of the drug discovery process can have lasting effects – and significantly impact the success or failure of hit candidates down the line.
Ultimately, more integrated expertise translates into more secure decision making.