Validating your therapeutic concept: From target selection to preclinical proof
Investigational therapeutics fail for many reasons, but a common cause of attrition is that the underlying biological hypothesis does not translate into meaningful clinical benefit, together with challenges in chemistry and formulation. The failure rates on almost 90% of drug candidates, whether by lack of efficacy or safety liabilities or lack of translational relevance, are indicative of underlying pitfalls in early therapeutic hypothesis validation. Validation of therapeutic concepts is a hypothesis-based approach that determines whether manipulation of a given biological target can causally and meaningfully affect human disease, not merely target engagement, but a mechanistic causality, dose-response correlation, and translatability. Robust preclinical validation, achieved through the integration of high-fidelity models, human genetic data, functional genomics, and mechanism-related markers, can lower downstream risk and improve prospects for clinical success. With increasingly limited budgets for research and development and heightened regulatory and investor scrutiny, has turned into a strategic principle necessary for the prioritization of programs with the greatest potential for patient benefit.1,3
Integrating target selection and validation
Target selection and validation in drug discovery are highly interconnected and iterative processes. The target selection starts with understanding disease biology through genomic associations, pathway analyses, and phenotypic screening. Validation aims to determine whether modification of the target will have a significant impact on the disease phenotype, thereby justifying therapeutic intervention. Among the key requirements that must be fulfilled to validate the role of a target in disease biology, strength of its mechanistic link to the disease process has to rank high. Genetic evidence in humans is one of the most valuable sources for establishing causal relationships; targets with genetic support indeed have a higher clinical success rate. However, an exclusive dependence on genetic associations is invariably insufficient, since many disease-related genes exhibit pleiotropic, context-specific, or developmental effects that might not be engaged by pharmacological interventions. This underlines the need for additional functional evidence.
Functional validation helps distinguish true disease-driving biology from signals that are merely correlative by experimentally perturbing targets and evaluating their effects on disease-relevant pathways and phenotypes. Since not every disease-related protein is a good therapeutic target, the extensive validation will demand unambiguous, reproducible, and dose-dependent effects that are in line with disease modification. Biological relevance should also be matched with drugs, and targets must be manipulated in a safe, effective manner with practicable therapeutic tools. Integrating target selection and validation therefore anchors drug discovery programs in causal disease biology while supporting clinical and translational feasibility.3,4
Designing preclinical proof-of-concept studies
A preclinical proof-of-concept (PoC) study is an early stage of development focused on evaluating a concept, mechanism, or application to confirm its useful functionality under controlled circumstances before engaging capital intensive drug or product development. In order to establish the theoretical foundation required for further prototype development, the step entails the interrogation of a conceptual model. As a result, several techniques and information sources are used to evaluate an idea’s potential and make decisions about whether or not to continue investing in more resource-intensive stages of development. PoC studies are meant to assess feasibility, bring to light early issues, refine assumptions, and shape study designs rather than to provide comprehensive solutions.5,6
Model selection
Translational success is largely dependent on Model Selection. Simple in vitro systems (e.g., immortalized cell lines) are easy to manipulate in an experimental setting, but they lack biological complexities. Highly complex in vitro models (e.g., primary human cells, organoids, and patient xenografts) are biologically more accurate; however, they can generate variability and suffer from a hih feasibility hurdle. Animal models remain indispensable, but their predictive validity varies widely, and many fail to fully mirror human responses in diseases like neurodegeneration and inflammation. Strong translational reliability of preclinical models is enhanced when these models are well selected based on mechanistic consistency with the disease-relevant pathways targeted by the intervention and well benchmarked in relation to competition, rather than on historical precedent. Additionally, using multiple combined orthogonal model systems helps demonstrate that the intervention produces consistent effects across different biological contexts.7
Biomarkers and endpoints
Biomarkers and Endpoints offer qualitative data of target interaction, pathway manipulation, and downstream biological responses which is crucial to translation. Mechanistically linked pharmacodynamic biomarkers, such as protein phosphorylation changes, transcriptional signatures, or metabolite alterations, confirm that and assesses to which extent a therapeutic engages its intended target. Target engagement biomarkers exhibit biological efficacy, whereas safety biomarkers identify potential on-target and off-target liabilities early. Biomarkers should be measurable in clinical samples (e.g., blood or tissue biopsies) to demonstrate proof-of-mechanism in human subjects, and inform dose selection, diminishing uncertainty as programs transition into Phase I trials and beyond.8
Study design considerations
Preclinical study design is an important aspect when it comes to ensuring the credibility and translatability of results attained. Poor study design, including small sample sizes, lack of randomization or blinding, or insufficient controls, could easily compromise study results, contributing to irreproducibility. Many of these risks can be minimized through careful experienced design, which includes appropriate statistical design, unbiased outcome assessment based on predefined success criteria, matching with realistic clinical endpoints and transparent reporting. Such approach increases confidence that preclinical results will successfully translate to human studies.9
Managing risk and decision‑making
There is always a degree of uncertainty in concept validation. However, a systematic risk-management model allows making informed decisions and avoid long-term investment in low-probability hypotheses. Such criteria must be prospective and periodically revised as new information is obtained so that the decisions are based on quantitative evidence and not on a posteriori judgment. Robust go/no-go criteria promote the quality of the internal decisions by formalizing the data used to guide the progression or termination decision. Quantitative methods that consider uncertainty in treatment effects and incorporate composite definitions of success, including a combination of statistical significance and clinical relevance and benefit-risk balance, provide a more comprehensive basis for advancing programs. Those programs where there are no explicit points of decision would evolve through inertia, increasing risk for wasting resources without improved confidence in their potential.
A balance between timelines and budgets should be adjusted to match the validation goals to prevent an under-/over-investment in exploratory activities. Long, unconcentrated exploratory cycles may lead to huge amount of data without answering the major uncertainties, whilst too narrowed timelines may lead to superficial validation that does not answer important questions. Adaptive investment schemes, where future funding depends on achieving specific scientific milestones, enhance portfolio productivity and resource allocation. They focus investment on the most promising assets and discontinue those that fail to meet critical criteria. Notably, any decision on termination of a program must not be interpreted as a failure but rather an indicator that the risk-management framework is performing as expected. Terminating programs with lower chance of success early makes resources available and improves overall research and development productivity, promoting a disciplined attitude towards translational science.10
Role of partnerships and external support
Collaboration with contract research organizations (CROs), researchers, and consulting scientists is common practice, as experienced support ensures greater operational control and access to appropriate institutional knowledge. Contract research provides access to optimized assays, disease models, and regulatory-compliant processes, thus crucially removing roadblock and improving chance of succes in many cases. Expert consulting provides disease-specific information and critical analysis of experimental design, especially when focusing on highly specialized areas. Importantly successful collaborations involve the proper formulation of scientific goals and the strict monitoring of data quality and interpretations.11
Preparing for clinical testing
Successful preclinical concept validation leads to readiness for first-in-human clinical testing through the integration of scientific rationale, regulatory strategy, and operational execution. Translational research bridges the gap between preclinical results and human outcomes, minimizing attrition during the initial clinical phases and maximising patient benefit. The regulatory demands today focus both on safety and therapeutic significance for test subjects. To identify novel targets and therapies, drug agencies demand Investigational New Drug (IND) packages to explicitly show target engagement, dose-response relationships, and a reasonable safety margin in human-relevant models. Preclinical evidence must be robust and consistent with clinical endpoints to enhance regulation confidence. Pre-IND meetings with regulators are advisable to ensure that study design is acceptable and to discuss expectations related to chemistry, manufacturing and controls (CMC), which leads to enhanced IND preparation and less risk of clinical progression.
A translational strategy utilizing validated biomarkers enhances the connection between preclinical models and human biology. Biomarkers can be, amongst others, indicative for mechanism of action, pharmacodynamics, and disease modulation, aiding in rational dose selection and cohort stratification during first-in-human trials. They provide quantitative or qualitative links between animal studies and clinical data, thus improving predictability and minimizing translation failures.
Next, manufacturing readiness remains a highly important but frequently ignored process that plays a critical role in translation into the clinic; early development stages should consider the process of scaling, stability, formulation, and cGMP compliance. Preclinical studies need to use material closely related to the product intended to enter the clinic to ensure consistent alignment between the pharmacology and CMC properties to facilitate IND and study initiation.
Finally, approved concepts should be communicated to the stakeholders in a clear manner, integrating biological rationale, translational evidence, and clinical strategy that underlines therapeutic potential and feasibility to support funding and partnerships and smooth translation to the clinic.8,12-15
Conclusion: from concept to candidate
Therapeutic concept validation is an evidence based, systematic, process which is the basis of an effective development of the drug. By integrating rigorous target selection, robust preclinical studies, and structured, milestone-based decision-making, organizations can reduce attrition and enhance translational success. Research findings have repeatedly demonstrated that the breakdown of late-phase clinical development is often linked to lapses in early validation. Programs that are developed on a robust biological basis, with reproducible preclinical data and mechanistically relevant biomarkers, exhibit greater chances of successful transition of concept to clinical candidate. In the current competitive and resource-intensive world, it is not only logical, but also paramount to validate at an early stage. An experienced and well-organized validation environment allows sound decision-making, allocating resources in the most effective way, and enhancing the likelihood of successful science to translate into safe and effective treatments of patients.
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Frequently asked questions about validating your therapeutic concept
What is therapeutic concept validation in drug discovery?
Therapeutic concept validation is the process of confirming that modulating a specific biological target can causally and meaningfully impact disease biology, with evidence that supports translation into clinical benefit.
Why do most drug candidates fail during development?
Most drug candidates fail due to insufficient early validation of the biological hypothesis, lack of translational relevance, or safety liabilities rather than issues with chemistry or formulation.
How are target selection and validation connected?
Target selection identifies disease-relevant biological targets, while validation confirms through functional and mechanistic studies that modifying the target produces a reproducible, dose-dependent effect relevant to human disease.
What is preclinical proof of concept in therapeutic development?
Preclinical proof of concept demonstrates that a therapeutic intervention engages its target and produces biologically meaningful effects in relevant experimental models, supporting progression toward clinical testing.
Which models are best for preclinical concept validation?
The most predictive validation strategies use multiple complementary models, including in vitro systems, human-relevant models such as organoids, and animal models selected for mechanistic relevance rather than historical use.
How do biomarkers support therapeutic validation?
Biomarkers confirm target engagement, pathway modulation, and biological response. Translational biomarkers measurable in both preclinical and clinical settings reduce uncertainty during first-in-human studies.
When should external partners or CROs be involved in validation?
External partners are valuable when specialized expertise, validated disease models, advanced toxicology, or regulatory-compliant studies are required to strengthen evidence and accelerate decision-making.
References
- Sun, D., Gao, W., Hu, H., & Zhou, S. (2022). Why 90% of clinical drug development fails and how to improve it?. Acta Pharmaceutica Sinica B, 12(7), 3049-3062.
- Calado, C. R. (2024). Bridging the gap between target-based and phenotypic-based drug discovery. Expert Opinion on Drug Discovery, 19(7), 789-798.
- Zhou, Y., Zhang, Y., Zhao, D., Yu, X., Shen, X., Zhou, Y., … & Zhu, F. (2024). TTD: Therapeutic Target Database describing target druggability information. Nucleic acids research, 52(D1), D1465-D1477.
- Jia, Z. C., Yang, X., Wu, Y. K., Li, M., Das, D., Chen, M. X., & Wu, J. (2024). The art of finding the right drug target: emerging methods and strategies. Pharmacological Reviews, 76(5), 896-914.
- Bailey, A. G., Reingold, B. M., Johnson, J. D., & O’Connor, A. C. (2025). Paths towards commercialization: evidence from NIH proof of concept centers. The Journal of Technology Transfer, 1-23.
- Yuan, L., Zhao, P., Zhang, J., Xu, X., Jin, M., Fang, Z., … & Li, M. (2024). Enhancing translational medical research through proof-of-concept services: clinicians’ perspectives. Scientific Reports, 14(1), 31108.
- Fosse, V., Oldoni, E., Gerardi, C., Banzi, R., Fratelli, M., Bietrix, F., … & PERMIT Group. (2022). Evaluating translational methods for personalized medicine—a scoping review. Journal of Personalized Medicine, 12(7), 1177.
- Hartung, T., King, N. M., Kleinstreuer, N., Leist, M., & Tagle, D. A. (2024). Leveraging biomarkers and translational medicine for preclinical safety: Lessons for advancing the validation of alternatives to animal testing.
- Vollert, J., Schenker, E., Macleod, M., Bespalov, A., Wuerbel, H., Michel, M., … & Rice, A. S. (2020). Systematic review of guidelines for internal validity in the design, conduct and analysis of preclinical biomedical experiments involving laboratory animals. BMJ open science, 4(1), e100046.
- Saint‐Hilary, G., Robert, V., & Gasparini, M. (2018). Decision‐making in drug development using a composite definition of success.Pharmaceutical Statistics, 17(5), 555-569.
- Strategic Outsourcing & Partnerships: How Companies Are Redefining R&D with CROs and Academic Collaborations. https://cataligent.in/blog/strategic-outsourcing-partnerships-how-companies-are-redefining-rd-with-cros-and-academic-collaborations/?
- Mahalmani, V., Sinha, S., Prakash, A., & Medhi, B. (2022). Translational research: Bridging the gap between preclinical and clinical research. Indian journal of pharmacology, 54(6), 393-396.
- Jeffers, M. S., Xi, C. E., Bapuji, R., Wotherspoon, H., Kimmelman, J., Bedford, P., … & Fergusson, D. A. (2024). Synthesizing regulatory guidance for demonstrating preclinical efficacy and translating promising cell therapies to early phase clinical trials: a scoping review. BMC medicine, 22(1), 487.
- Drug Development and Review Definitions. https://www.fda.gov/drugs/investigational-new-drug-ind-application/drug-development-and-review-definitions?
- Translational Research: Pre-Clinical Development. https://www.niddk.nih.gov/research-funding/research-programs/translational-research-therapeutic-discovery-development/pre-clinical-development?