The development of a new drug is a complex, multidisciplinary, costly, and time-consuming process, often spanning over a decade with the estimated costs ranging from hundreds of millions to several billions, depending on the methodology used, the underlying assumptions, and the products under review. Historically, new drugs have been discovered through the random screening of a large number of analytical assays of active ingredients from natural sources, followed by validation of the hits for activity. In fact, every successful biomedical science begins with the right assay, which measures a biological process in a physiologically relevant and robust manner. The assays can be performed at the whole animal, cell-based, or molecular levels. Well-designed assays are instrumental in helping researchers identify molecules with the desired therapeutic effect while filtering out ineffective ones. Although assays vary substantially with respect to design, speed, throughput, and complexity, the key principle is that each assay must be fit-for-purpose, meaning it should be appropriately aligned with the specific question being addressed at each stage of the drug discovery process. Classically the balance between feasibility in the early stages and biological relevance in the later stages is the key to the development of accurate, efficient, and robust assays for an effective drug discovery campaign.1-3
The selection of an appropriate assay is crucial to drive drug discovery from the successful exploratory phases to the clinical testing of drug candidates. Poorly designed assays are susceptible to experimental interference and technical artifacts, frequently yield misleading results that lead to false-positive or false-negative hits. These errors misguide research direction, waste precious time and resources, and negatively impact the pace of therapeutic progress. In contrast, it is important to recognize that technical robustness is only one dimension. A technically robust and well-validated test may still be inadequate if it is predicated on an erroneous biological notion, examining a target or pathway that does not accurately represent the disease in question. Consequently, conceptual design and biological significance are equally crucial. When all of these elements are established, assays enhance the quality of data, reproducibility, and confidence that screening hits are reflective of biological activity. Robust assays facilitate identification of reliable hits, efficient lead optimization, and improved decision-making throughout the discovery pipeline, while automation further increases precision and minimizes variability in experimental findings. Industry guidelines also stress that bioassays have to be reproducible, accurate, and biologically relevant, as any inaccuracy in the experiments can cause false results and unreliable dose-response relationships, thus delaying the development of drugs All these factors highlight the importance of bioassays in providing accurate data, thus facilitating the progression of hits to leads, as well as increasing the probability of translating discoveries into viable drugs.3-7
An assay is an analytical measurement procedure defined by a set of reagents that produces a detectable signal to quantify the biological process. The selection of the appropriate type of assay during the drug discovery process is critical to obtain meaningful data and to avoid false leads in the drug discovery process. Drug discovery involves the use of a cascade of experimental assays, either cell-free (biochemical) or cell-based, to determine the interaction of drug candidates with the target systems. These drug candidates can include small molecules, biologics, antibodies, and cell and gene therapies. Assays of different types offer complementary data ranging from the molecular level to the cellular and whole-animal level. The choice of either a biochemical or cell-based assay, along with the particular detection format, is a matter of preference.
Biochemical assays are used to measure the binding affinity or inhibitory activity of the tested drug candidate with the target biological entities, such as enzymes, receptors, or protein complexes. These tests are common in drug discovery at the initial stage as they offer quantitative and accurate measurements of enzyme activity, kinetics of inhibition, and interactions under rigorously controlled experimental settings. They are sensitive, and compatible with high-throughput screening, making them ideal for rapid screening of large compound libraries. However, due to the absence of cellular context in biochemical assays, they are frequently used in conjunction with cell-based assays to validate biological relevance down the line.
Cell-based assays measure compound activity in living cells, allowing researchers to measure desired biological processes such as gene expression, cell signaling, cell proliferation, and cytotoxicity. Common examples include reporter gene assays that allow scientists to quantify pathway activity, viability and proliferation assays that assess compound effects on cell survival and growth, apoptosis assays that measure programmed cell death, and other functional formats evaluating phenotypic or mechanistic cellular responses. In oncology research, viability and proliferation assays are predominantly employed to evaluate antiproliferative or cytotoxic efficacy rather than safety alone. In comparison to other isolated biochemical systems, these experiments can produce more relevant results because they maintain the complexity of cellular surroundings. Cell-based assays are therefore essential for confirming results and directing lead optimization.
Phenotypic assays aim to quantify the variations in observable biological attributes, including cell morphology, differentiation, migration, or survival, informing on the impact of drug induced functionality without prior knowledge of a particular molecular target. These assays are commonly applied in complex cell models and whole-organism systems. Phenotypic assays offer the true representation of biological systems by capturing integrated biological responses and enable the identification of compounds with novel mechanisms of action. Although phenotypic screening can suffer from considerable challenges towards target identification and safety profiling, since it was first applied, phenotypic read-outs have played a key role in the discovery of first-in-class drugs and remains particularly valuable for uncovering novel therapeutic pathways.
Biophysical and binding assays offer direct, label-free measurements of molecular interactions, providing detailed information on binding kinetics, affinity, and thermodynamics. Methods such as surface plasmon resonance allow real-time monitoring of biomolecular interactions, and isothermal titration calorimetry helps determine energetic elements that drive binding events. These techniques are crucial for validating screening hits, verifying direct target engagement, and directing structure-based drug design. Their enhanced analytical reliability renders them particularly useful in the processes of validation of hits and optimization of leads.3,8-12
Advancements in stem cell biology, genome editing, and microfluidic engineering are driving assay technology towards higher physiological relevance and improved predictive accuracy. Instead of using classic immortalized cell lines, researchers are adopting induced pluripotent stem cells (iPSCs) and primary human cells, which better reflect the performance of actual human tissues, facilitating improved translation across neurological, cardiovascular, and metabolic studies. CRISPR genome editing further complements these models by enabling researchers to insert or repair specific genetic alterations within cell models. This generates isogenic cell lines (cells that are genetically identical except for a single controlled mutation), enabling precise studies of disease mechanisms and drug responses. These genetically programmed models enable testing of therapies in a human relevant environment that enhances the predictive value of preclinical studies. These advances are coupled with a resurgence of interest in phenotypic assays due to their potential to resolve complex, system-wide cellular responses. By observing how cells react as an integrated system, rather than focusing on a single molecular target, phenotypic screening has been instrumental in discovering first-in-class drugs for challenging and genetically complex diseases. The development of organ-on-a-chip and micro physiological systems has enhanced predictive toxicity testing alongside iPSC and CRISPR-based models. These models offer a more realistic environment to study the effect of medication, modeling key features of human tissues, including dynamic fluid flow, 3D architecture, and organ-organ interactions. By introducing patient-specific cell types into the chips researchers can replicate human drug metabolism, determine potential toxicities such as drug-induced liver or cardiac toxicity, and test efficacy in a human-relevant environment prior to clinical trials. All these developments, from organ-on-a-chip systems to physiologically relevant cell models and CRISPR-edited lines, are transforming the assay development process, enhancing the translational accuracy of assays, reducing late-phase failure, and accelerating drug development.12-15
A crucial aspect of the drug discovery process is the choice of the appropriate assay since the quality of the data directly affects the compounds that will proceed forward. The fundamental principle underlying this choice is biological relevance: the assay must accurately reflect the target and the disease situation. Employing models, like primary human cells, patient-derived models, or 3D tissue constructs, can serve to ensure that observed responses accurately represent real biology. Confirmation of the assay using known reference compounds gives confidence that the readouts of the assay are predictive and meaningful. Besides relevance, other practical factors that are important to consider include throughput, affordability, and scalability. Although more complex assays, such as organoids or detailed biophysical analyses, are better suited for later-stage lead optimization, where mechanistic information is essential, high-throughput formats enable rapid screening of huge screening libraries. Achieving the best possible balance between complexity and efficiency ensures that resources are used efficiently without undermining biological integrity. Equally important is robustness and reliability. An optimal assay is one that is reproducible and sensitive enough to detect true biological change while reducing false positives or false negatives. Compatibility with downstream research is also necessary, assays must tolerate conventional chemicals, such as DMSO, and be adaptable for follow-up mechanistic studies. The use of controls to detect non-specific compounds guarantees that only true leads are pursued. When all these factors are considered, the assays not only produce high-quality, actionable data, but also simplify the identification of hits, accelerates development of leads, and increase the probability that promising candidates will succeed in the clinic.7,8,16,17
Selecting the right assay is vital to accelerating drug discovery and ensuring relevant results. From biochemical and cell-based studies to phenotypic screens, iPSC models, CRISPR-edited lines, and organ-on-a-chip systems, each technique gives various information on compound efficacy and safety. By customizing tests to reflect disease biology, maximize repeatability, and facilitate downstream analysis, researchers can uncover true hits while eliminating false leads. Collaborating with specialized suppliers or leveraging contemporary platforms can further increase test design. Investing in specialized, well-validated assays ultimately enhances the likelihood of turning early findings into clinically viable treatments.
An assay is an analytical measurement procedure defined by a set of reagents that produces a detectable signal for quantifying a biological process.
The main types of assays are biochemical and cell‑based assays. Cell-based assays measure compound activity in living cells, while biochemical assays measure direct interactions like enzyme activity under controlled conditions.
Biological relevance guarantees the assay represents true disease biology, so reported effects are significant and more likely to predict patient responses. Using suitable cell types or human tissue models improves translational accuracy.
Phenotypic assays aim to quantify the variations in observable biological attributes, including cell morphology, differentiation, migration, or survival, without prior knowledge of a particular molecular target.
These technologies enable more physiologically relevant, patient-specific, and predictive models that improve translational accuracy, enhance toxicity prediction, and reduce late-stage clinical failures.
Biological relevance, throughput, cost, scalability, robustness, reproducibility, and compatibility with downstream mechanistic research are among the key concerns that should be addressed.
Customized assays, tailored to target biology and project goals, provide high‑quality data, improve hit identification, and minimize downstream failures, thus increasing the probability of developing effective drug candidates.
