Target validation as a decisive success factor in modern drug development

From hypothesis to biological evidence

Target validation has evolved into a multidisciplinary process that brings together several scientific disciplines. While researchers previously relied on a single dominant hypothesis or a limited number of experimental models, the focus today is on converging evidence from multiple independent sources.

Important sources include:

• Human genetics and naturally occurring genetic variation;

• Transcriptomics, proteomics and other multi-omics datasets;

• Translational research;

• Patient-derived models;

• Biomarkers;

• Real-world clinical data.

  
  

The more strongly these different lines of evidence support one another, the greater the confidence that the target plays a causal role in the disease process.

This integrated approach not only reduces scientific uncertainty, but also increases the likelihood that research findings can ultimately be translated into clinical applications.

Human biology as the new standard

One significant development in modern drug discovery is the growing emphasis on human biology.

Historically, animal models provided the primary basis for target selection. Although they continue to play an important role, it is increasingly clear that many biological mechanisms do not translate directly to humans. This partly explains why many programmes that appear promising during preclinical development ultimately fail in clinical trials.

Organisations are therefore investing increasingly in genetic datasets, patient-derived materials, organoids, advanced cell models and other methods that more accurately represent human disease biology.

The earlier biological hypotheses can be confirmed in a human context, the greater the likelihood of successful clinical development.

Artificial intelligence changes validation methods, not biology

Artificial intelligence has significantly accelerated target identification and validation. Machine learning models can analyse millions of publications, identify genetic patterns and detect potential interactions that would be extremely difficult for researchers to discover manually.

AI does not, however, change the fundamental challenge.

Algorithms generate hypotheses. They do not provide biological evidence. Experimental validation remains necessary to establish whether a target is genuinely therapeutically relevant.

The most successful organisations therefore use AI not as a replacement for scientific expertise, but as an instrument for asking better questions, discovering relationships more rapidly and defining research priorities more precisely.

Target validation requires multidisciplinary decision-making

Effective target validation is not solely the responsibility of discovery biology. Successful decision-making depends on integrating different areas of expertise.

Biologists, chemists, bioinformaticians, translational researchers, clinical experts and biomarker specialists each bring a different perspective to the same question. This combination enables organisations to challenge scientific assumptions critically before initiating substantial development programmes.

Organisations that establish this multidisciplinary collaboration at an early stage are better able to identify risks, investigate alternative hypotheses and make well-informed investment decisions.

The strategic value of early certainty

In an environment where drug development costs continue to rise and pressure to innovate is increasing, target validation is becoming a strategic discipline.

Every uncertainty resolved early in the process prevents exponentially higher costs during subsequent development phases. Target validation is therefore not only a scientific activity, but also an essential component of portfolio management and risk management.

For research and development leaders, this means the quality of the earliest scientific decisions can ultimately determine the strength of the entire innovation pipeline.