3 reasons why you should care about target validation

By Daniel Schmid, MSc.-MBA and Liliane Goetsch, PhD

Target identification and validation are often seen as the first step of the drug discovery process. However, they may also be the second step in case of compounds are discovered via phenotypic screenings.

Here are the 3 main reasons why you should care about target validation:

1) You could end up choosing a target which is not disease-modifying

We've seen it over and over again. A great example is targeting proteins related to the biogenesis of Beta-amyloid for the treatment of Alzheimer's disease. For Alzheimer's clinical research, it has meant more than three decades of constant failures in Phase 2 and Phase 3 by high-profile, well-funded pharmaceutical companies. It is clear that choosing a target that is associated but not clearly causative and necessary for the resolution of the disease, can lead to later-stage failures and wasted funds. In the example of Alzheimer's clinical research, the results show that choosing targets along the beta-amyloid are non disease-modifying targets and therefore, not going to enable successful drug discovery.

When validating targets, special attention needs to be given to the evaluation of causation/correlation and necessity of the target for the development/resolution of the disease. A good explanation is provided by Dr. William Kaelin, Nobel Laureate physician-scientist and professor of medicine at Harvard University on his article "Common pitfalls in preclinical cancer target validation" (2017):

"Two things (A and B) can correlate with one another for several reasons. For example, A might cause B, B might cause A, or perhaps both statements are true. However, there are many examples in biology where two things that correlate with one another do not have a causal relationship (that is, A does not cause B and B does not cause A). Sometimes this occurs because both A and B are under the control of a confounder, X, that causes both of them. Other times this is because A and B simply correlate with one another by chance"

In addition, "failure to distinguish between necessity and sufficiency can also lead to illogical conclusions. By way of analogy, it is necessary for all of the tumblers to be in place for a combination lock to open, and it is therefore sufficient for any one of the tumblers to be out of place to prevent the lock from opening".

2) You will not be able to choose the right dose for your clinical studies based on your preclinical data

By the time you reach Phase 2, and unless your Phase 1 studies have been conducted on diseased patients, most of your understanding of the pathology and response will be based on animal studies.

Knowledge regarding your target will allow you to understand the translation from animal species to humans.The main questions to answer are related to the comparability of the target expression profile between animal models and humans as well as the possible differences in the target biology between species (Rudmann, 2012). For example, when animals naturally express the identified target, it is not always clear that the target distribution and function is representative of the human situation. The context is even more complicated when the targeted component is not naturally expressed in the selected species.

 Problems can arise when animal models express genes with low homology or the function in the model is carried by more than one redundant gene. One example is the case for the murine CXCR/CXCL network which is not exactly the same as the human one (e.g. the CXCL8 gene does not exist in mice where MIP-2 (mCXCL2), KC (mCXCL1) and LIX (mCXCL5) are generally considered functional homologous of hCXCL8) (Zlotnick, 2012).

 A good understanding of the target biology allows for accurate biomarkers of efficacy to evaluate in vivo target engagement. It is mandatory to define a robust PK/PD model to support the dose range selection for phase I and II. Choosing those biomarkers requires a strong understanding of how target manipulation modulates signalling pathways to improve the disease. Awareness of the differences in the target biology between animals models and humans will enable careful corrections to the model in order to select the right, efficacious doses for clinical studies.

In the case that you obtain initially weak or discouraging results from your Phase 2 studies or even during pre-clinical studies, if you lack proper target validation, you will not be able to determine the cause with confidence. It will be difficult to assess if the source of your problem is 1) a lack of exposure at the site of action, 2) weak binding of your candidate or 3) irrelevance of the target. This will in turn affect your capacity to problem solve during the development process (Emmerich, 2020, Gashaw, 2011).

Not properly identifying the source of your problems will decrease your confidence when selecting the right time to stop development.

3) You will not be able to forecast on-target side effects from your mechanism of action

It's worth remembering that side effects cannot only come from a therapeutic binding to obscure, unknown receptors at undesired physiological locations but can arise from the modulation of the intended target. Therefore, knowledge of the biological pathways associated with the target will enable better prediction, monitoring and management of potentially arising side effects.

Important attention should be given to the exploration of the available evidence regarding the side effects that could arise from the modulation of the target.

It is therefore important to explore the targets deeply in order to answer (Emmerich, 2020):

1. Is the target selective and not genetically linked to other diseases?
2. Is there prior knowledge on safety of the target or reported evidence for the role of the target in a known pathway and/or physiological process that may be harmful if disrupted?
3. Are safety biomarkers available and can adverse effects be monitored and/or predicted by safety biomarkers?
4. Is there sufficient confidence that a necessary safety window has been or can be established?

In case your project is searching for VC funding, questions regarding the forecasted safety profile of your target will keep arising by potential investors who will be more hesitant to pull the trigger on projects with insufficient target validation.

Conclusion

The above cited reasons to pay attention to target validation have been described by groups who have summarised the reasons for project failures in pharma over the last decades. While it is possible to advance a project with incomplete knowledge regarding the target, securing the basic and key knowledge around the target on the process known as "target validation" will ensure that your project has above-average chances of success. If knowledge around your target is scare or unavailable, it should be considered a red-flag and strategies should be put in place to address the uncertainty as early as possible.

References

• Emmerich, C. H., Gamboa, L. M., Hofmann, M. C. J., Bonin-Andresen, M., Arbach, O., Schendel, P., Parnham, M. J. (2020). Improving target assessment in biomedical research: the GOT-IT recommendations. Nature Reviews Drug Discovery. doi:10.1038/s41573-020-0087-3 
• Gashaw, I., Ellinghaus, P., Sommer, A., & Asadullah, K. (2012). What makes a good drug target? Drug Discovery Today, 17, S24–S30. doi:10.1016/j.drudis.2011.12.008 
• Kaelin, W. G. (2017). Common pitfalls in preclinical cancer target validation. Nature Reviews Cancer, 17(7), 425–440. doi:10.1038/nrc.2017.32 
• Lansdowne, L.E. (2018). Target Identification & Validation in Drug Discovery. https://www.technologynetworks.com/drug-discovery/articles/target-identification-validation-in-drug-discovery-312290
• Oxford, A. E., Stewart, E. S., & Rohn, T. T. (2020). Clinical Trials in Alzheimer’s Disease: A Hurdle in the Path of Remedy. International Journal of Alzheimer’s Disease, 2020, 1–13. doi:10.1155/2020/5380346 
• Rudmann, D. G. (2012). On-target and Off-target-based Toxicologic Effects. Toxicologic Pathology, 41(2), 310–314. doi:10.1177/0192623312464311 
• Zlotnik A. &, Osamu Y. (2012). The chemokine superfamily revisited. Immunity, 36(5), 705-716. doi: 10.1016