Phenotypic Screening: A Powerful Tool for Drug Discovery

Over the past few decades, target-based drug discovery has dominated the pharmaceutical landscape, focusing on screening compounds against specific molecular targets. However, this approach has limitations, including failures in clinical trials due to poor correlation between mechanistic targets and the actual disease state.¹ Phenotypic screening has re-emerged as a powerful strategy for identifying bioactive compounds based on their observable effects in cells, tissues or whole organisms – without requiring prior knowledge of a specific molecular target.²

Phenotypic screening played a crucial role in early drug discovery efforts, where it was used to develop numerous first-in-class therapeutics, including antibiotics, anticancer drugs and immunosuppressants.³ Historical accounts state that Alexander Fleming’s discovery of penicillin in 1928 involved observing the phenotypic effect of Penicillium rubens on bacterial colonies. Fleming noticed that bacteria near the mold were dying, representing one of the first examples of phenotypic screening.⁴

The resurgence of phenotypic screening in modern drug discovery is driven by advances in high-content imaging, artificial intelligence (AI)-powered data analysis and the availability of physiologically relevant models, such as 3D organoids and patient-derived stem cells.⁵ These innovations have enhanced the efficiency and scalability of phenotypic screening, making it a valuable tool for identifying new drugs with novel mechanisms of action.⁶

What is phenotypic screening?

Phenotypic screening is a drug discovery approach that identifies bioactive compounds based on their ability to alter a cell or organism’s phenotype (observable characteristics) in a desired manner. Unlike target-based screening, which focuses on compounds that interact with a specific molecular target, phenotypic screening evaluates how a compound influences a biological system as a whole. This approach enables the discovery of novel mechanisms of action, particularly in diseases where the molecular underpinnings remain unclear.

Genotype vs phenotype

Understanding the difference between genotype and phenotype is key to appreciating the role of phenotypic screening in drug discovery. Genotype refers to an organism’s genetic makeup – its DNA sequence – while phenotype encompasses the observable traits that arise from genetic and environmental interactions.

In target-based screening, drug discovery efforts often begin with looking at a genotype, where a known genetic mutation or molecular target is selected, and compounds are screened for their ability to modulate this specific target. This strategy is widely used in rational drug design but has limitations, particularly for diseases with complex, polygenic origins.

Phenotypic screening, in contrast, focuses on functional outcomes. Rather than requiring a predefined target, compounds are tested based on their ability to elicit a desired biological response. This approach allows for discovering first-in-class drugs by revealing unexpected mechanisms of action that may not have been identified through genotype-driven methods. As a result, phenotypic screening is particularly valuable in areas such as cancer therapy, neurodegenerative disease research and regenerative medicine, where complex cellular interactions play a critical role in disease progression.⁶

Key steps in phenotypic screening

Below is an example of the typical steps taken in a phenotypic screening workflow:

1. Selection of the biological model: Choosing an appropriate system, such as cell-based assays, organoids or in vivo models, to screen drug candidates.
2. Application of compound libraries: Testing diverse chemical libraries to observe biological effects. Non-annotated compounds with high structural heterogeneity are often prioritized to maximize novel target discovery.
3. Observation and measurement of phenotypic changes: Utilizing techniques such as high-content imaging, flow cytometry or biochemical assays to assess changes.
4. Data analysis and identification of active compounds: Using AI-driven image analysis and statistical modeling to identify hits.
5. Counter screening and toxicity profiling: Early-stage counter screens can exclude nonspecific hits using cytotoxicity panels.
6. Target deconvolution and validation: Once a compound exhibits a promising effect, mechanism-of-action studies are performed to determine how it works.

Types of phenotypic screening

Phenotypic screening can be broadly categorized into in vitro (cell-based assays) and in vivo approaches, each offering unique advantages and applications in drug discovery. The choice of screening method depends on factors such as the complexity of the biological system, the type of disease being studied and the desired level of physiological relevance.

Cell-based assays (in vitro)

In vitro phenotypic screening involves testing compounds on cultured cells to assess their effects on cellular functions, morphology or viability. This approach enables high-throughput screening of large compound libraries, making it a widely used method in early-stage drug discovery.

Key types of in vitro models:

• 2D monolayer cultures – Traditional cell culture models where cells grow as a single layer. Used for cytotoxicity screening and basic functional assays.
• 3D organoids and spheroids – More physiologically relevant models that better mimic tissue architecture and function. Commonly used in cancer and neurological research.⁹
• iPSC-derived models – Induced pluripotent stem cells (iPSCs) differentiated into specific cell types, enable patient-specific drug screening and disease modeling.
• Patient-derived primary cells – Derived directly from patients, offer a less complex approach to disease modeling.
• Organ-on-chip models – Recapitulate human physiological processes by merging cell culture with microengineering techniques inside mini cell culture microfluidic chips.

Advantages of in vitro screening:

• High-throughput capability – Allows rapid screening of thousands of compounds.
• Cost-effective and scalable – Typically requires fewer resources compared to in vivo models.
• Controlled conditions – Enables precise manipulation of experimental variables.

Limitations of in vitro screening:

• Lacks full physiological complexity – Simplified cell systems may not fully replicate human organ function.
• Limited drug metabolism assessment – Does not account for how compounds are processed in a whole-organism environment.
• May not capture long-term effects – Lacks systemic interactions present in living organisms.

In vivo phenotypic screening

In vivo screening involves testing drug candidates in whole-organism models to observe their effects in a systemic biological context. This approach provides insights into drug absorption, metabolism, distribution and toxicity that cannot be obtained from cell-based models.

Key types of in vivo models:

• Zebrafish – Small vertebrate model with high genetic similarity to humans. Used for neuroactive drug screening and toxicology studies.¹⁰
• Caenorhabditis elegans – Simple, well-characterized organism widely used in neurodegenerative disease research and longevity studies.
• Rodent models – Gold-standard mammalian models in preclinical research. Provide robust data on pharmacodynamics and pharmacokinetics.
• Non-human primates – Given their physiological and genetic similarities to humans, they are used in disease modeling, especially in neurodegenerative disorders and infectious disease research.
• Drosophila melanogaster – Also known as fruit flies, are used for high-throughput phenotypic screening based on their conserved genetic pathways with humans, short life-cycle and high reproductive rate.

Advantages of in vivo screening:

• More physiologically relevant than simplified cell systems – Provides a comprehensive understanding of drug effects in an intact system.
• Better predictive value for clinical trials – Captures systemic drug responses that in vitro models may overlook.
• Captures complex interactions – Accounts for metabolism, immune responses and long-term effects.

Limitations of in vivo screening:

• Lower throughput – Screening is slower and more resource-intensive than in vitro models.
• Ethical and regulatory considerations – Requires adherence to strict animal welfare guidelines.
• Species differences – Drug responses in animal models may not always translate directly to humans.

Phenotypic screening vs target-based drug discovery

Both phenotypic screening and target-based drug discovery have distinct advantages and limitations (Table 1), and their complementary use can enhance the efficiency and success of drug discovery efforts. One of the main differences is that phenotypic screening evaluates compounds based on observable biological effects, as a result, prior knowledge of molecular targets isn’t necessary (Figure 1).

Figure 1. Phenotypic screens identify active compounds that induce a change in a cellular or physiological phenotype. By embracing a broader target space researchers can study the effects of drugs on complex biological systems without prior knowledge of those targets. Credit: Technology Networks.

Target-based drug discovery involves selecting a well-characterized molecular target, often a protein or enzyme, and screening compounds that selectively bind to or modulate this target.

Advantages of target-based drug discovery:

• Mechanistic clarity – Researchers can design drugs based on well-defined molecular interactions.
• High specificity – Compounds are selected to precisely modulate a known target, reducing off-target effects.
• Efficient structure-based drug design – Advances in computational modeling and crystallography aid rational drug design.

Limitations of target-based drug discovery:

• Limited ability to discover novel mechanisms – Since the approach is hypothesis-driven, it is biased toward existing knowledge.
• Failure to capture complex biological interactions – Many diseases involve multifactorial pathways that cannot be fully understood through single-target modulation.

In contrast, phenotypic screening evaluates compounds based on observable biological effects rather than predefined molecular interactions. This method allows for the discovery of first-in-class drugs by identifying compounds that modulate disease processes in unexpected ways.

Advantages of phenotypic screening:

• Unbiased discovery of novel mechanisms – Compounds are selected based on their therapeutic effect, allowing for mechanistically novel drug discovery.
• Can capture complex biological mechanisms– Phenotypic screening captures complex biological interactions that can contribute to unexpected activity and toxicities, improving the likelihood that hits will translate to clinical efficacy, especially for complex diseases such as neurodegenerative disorders.¹¹
• Broad applicability – Useful in disease areas with unknown molecular drivers, such as neurodegenerative disorders and rare diseases.

Limitations of phenotypic screening:

• Mechanism of action (MoA) deconvolution – Once a hit is identified, additional studies are required to determine its molecular target.
• Lower specificity – Without a predefined target, off-target effects may be harder to predict.
• More complex screening assays – Requires advanced technologies like high-content imaging, multiomics and functional genomics to extract meaningful data.

Table 1. A comparison of phenotypic and target-based screening.

	Phenotypic screening	Target-based screening
Approach	Identifies compounds based on functional biological effects	Screens for compounds that modulate a predefined target
Discovery bias	Unbiased, allows for novel target identification	Hypothesis-driven, limited to known pathways
Mechanism of action	MoA is often unknown at discovery, requiring later deconvolution	MoA is defined from the outset
Technological requirements	Requires high-content imaging, functional genomics and AI	Relies on structural biology, computational modeling and enzyme assays

 Rather than competing approaches, phenotypic and target-based screening can work together to improve drug discovery outcomes. Many modern programs integrate phenotypic screening for initial hit identification, followed by target deconvolution using genomics and proteomics techniques.¹²

Phenotypic screening in drug repurposing

Drug repurposing, also known as drug repositioning, is the process of identifying new therapeutic uses for existing drugs. This approach has gained significant traction in recent years as a strategy to accelerate drug development, reduce costs and mitigate the high failure rates associated with de novo drug discovery.¹³ Phenotypic screening plays a critical role in drug repurposing by enabling the identification of unexpected therapeutic effects in disease models, independent of prior knowledge of a drug’s molecular target.

Key advantages of drug repurposing:

• Accelerates clinical translation – Repurposed drugs have established safety profiles, reducing regulatory hurdles and development time.
• Cost-effective – Leverages existing pharmacokinetic and toxicity data, cutting down early-stage R&D expenses.
• Beneficial for rare and neglected diseases – Provides new treatment options where limited therapeutic interventions exist.

Several drugs have been successfully repurposed through a combination of approaches, including phenotypic screening, which has demonstrated its ability to uncover new therapeutic indications.

To identify an inexpensive oral drug for treating sickle cell disease, researchers performed a high-throughput phenotypic screen of the ReFRAME drug repurposing library. This identified 106 compounds with antisickling activity. These compounds, tested on red cells from sickle trait individuals, showed promising results in inhibiting sickling.¹⁴

Advancements in AI, machine learning and big data analytics have significantly improved the ability to predict and validate drug repurposing candidates. AI-powered phenotypic screening platforms integrate large datasets from:

• High-content imaging – Automated analysis of cellular and morphological changes.
• Transcriptomics and proteomics – Identifies drug-induced gene and protein expression patterns.
• Electronic health records and real-world data – Detects unexpected clinical benefits from existing drugs.¹⁵

An AI-driven phenotypic screen helped identify Abaucin as a promising antibiotic to combat drug-resistant Acinetobacter baumannii, showcasing AI’s potential to uncover novel antibiotics, thereby offering alternative treatment options.¹⁶

The future of phenotypic screening in drug discovery

Phenotypic screening has long been a driver of first-in-class drug discovery, leading to the development of breakthrough treatments across multiple therapeutic areas. Advances in AI, multiomics technologies and 3D cellular models are refining this approach, making it more scalable, precise and efficient. As these technologies evolve, phenotypic screening is becoming an increasingly powerful tool for identifying novel therapeutics and accelerating drug development.

Integration of AI and machine learning

The large-scale data generated by phenotypic screening requires advanced computational tools for analysis. AI and machine learning are transforming drug discovery by automating data processing, pattern recognition and predictive modeling. These technologies can analyze high-content imaging data, detect subtle phenotypic changes and predict drug toxicity and efficacy more accurately than traditional methods. AI-driven approaches can also enhance target deconvolution, helping researchers uncover the molecular mechanisms underlying a drug’s phenotypic effects. AI-driven platforms also integrate multiomic datasets, such as transcriptomics and metabolomics to predict polypharmacology risks during early lead optimization, assisted by the use of principal component analysis to reduce dimensionality as well as neural networks to classify compounds by mechanism.

Advancements in 3D cellular models

Traditional 2D cell culture models often fail to replicate the complexity of human tissues, limiting their predictive value in drug screening. The development of 3D cellular models, organoids and organ-on-a-chip systems is significantly improving the physiological relevance of phenotypic screening. These models more accurately mimic tissue architecture, cellular interactions and disease pathology, allowing for better prediction of drug responses in humans.¹⁷

Multiomics approaches for deeper insights

Integrating multiomics technologies, including genomics, transcriptomics, proteomics and metabolomics, is expanding the potential of phenotypic screening. By capturing a comprehensive molecular profile of drug responses, these approaches allow researchers to identify biomarkers, elucidate mechanisms of action and improve target deconvolution.

CRISPR-based functional genomics is also contributing to more efficient target identification in phenotypic screening. By systematically knocking out genes and observing changes in phenotypic responses, researchers can rapidly determine which molecular pathways are essential for a drug’s activity.¹⁸

Automation and high-throughput screening innovations

The future of phenotypic screening is likely to be driven partly by automation, miniaturization and high-throughput capabilities, allowing for more efficient and cost-effective screening of large compound libraries. Advances in robotic microscopy, microfluidics and cloud-based data processing could enable researchers to conduct thousands of parallel experiments with minimal reagent consumption. These innovations have the potential to reduce experimental variability, increase screening throughput and accelerate drug discovery timelines.¹⁹

This content includes text that has been generated with the assistance of AI. Technology Networks' AI policy can be found here.

The above content has been fact checked by Dr. Aron Gyorgypal.

About the reviewer

Dr. Aron Gyorgypal is a postdoctoral research fellow at Harvard Medical School and Massachusetts General Hospital in Boston, Massachusetts. Traditionally trained as an engineer, Aron received his PhD in chemical and biochemical engineering from Rutgers University in New Jersey, where he studied antibody glycosylation in the context of bioprocessing to produce more homogeneous biologics. He utilizes his expertise as a consultant for biopharmaceutical companies to help better develop biosimilars. Following the completion of his PhD, he joined the Anthony Lab at Harvard Medical School and Massachusetts General Hospital in the immunology department, where he leverages his engineering skillset to deepen the understanding of immunological disorders and develop therapeutic solutions against such conditions. Some key topics Aron is interested in are oncology, immunology, neurobiology and glycobiology.