The Impact of Drug Target Instability Across Patient Populations

The development of effective therapeutics relies heavily on the stability of drug targets across diverse patient populations. However, when a drug target is unstable across these populations, it can significantly impact the efficacy of the drug in clinical trials and real-world applications.

Understanding Drug Target Instability

Drug target instability refers to the variability in the presence, structure, or function of a drug target across different individuals. This variability can be due to genetic differences, environmental factors, or a combination of both. When a drug target is unstable, the biology of the disease can become variable across a patient population. This inconsistency in biological networks across disease populations has profound consequences for clinical pharmacology. the drug  that has been specifically designed to target an unstable molecule may not consistently interact with its intended target or the consequences of engaging the target may not be consistent across a clinical population, leading to variable therapeutic outcomes. The ability to anticipate this stability is key to predicting the success of a drug designed to inhibit a given molecular target in a clinical trial or across a patient population.

How many clinical trials have failed in phases 2 or 3 due to a lack of stable representation of the drug target molecule across the patient population involved in a trial? We do not know. How many clinical trials are initiated with a strategy which affords a good degree of confidence that a drug target molecule is stably represented across the patient population selected to test in a given trial? None? Not enough? Not many? Again, this isn’t clear.

Cystic fibrosis, a genetic disorder that affects the lungs and digestive system, is caused by mutations in the CFTR gene, and treatments like CFTR modulators have been successful across patient populations. Why have CFTR modulators been so successful? Because the disease biology is consistent across the patient population and CFTR is stable and can be targeted with confidence. Contrast CF with cancer, a highly heterogeneous disease with multiple genetic and environmental causal factors driving its progression in patients.  Inhibitors of theoretically strong target candidates such as EGFR, BRAF, HER2 and VEGF proteins have all failed in multiple cancer types largely due to excessive variability of the expression and contribution of these molecules in a given population tested in clinical trial scenarios.

Instability in Clinical Trials

In clinical trials, drug target instability can lead to skewed results. If the clinical trial population is not selected for stable drug target expression then it will fail. If the trial population is selected diligently but does not adequately represent the diversity of the patient population, the results may not accurately predict the drug’s efficacy and safety in the broader population. This can lead to drugs being approved based on trial results, only to have reduced efficacy or increased side effects in the real-world patient population.

Addressing Drug Target Instability

Addressing drug target instability requires a comprehensive understanding of the genetic and environmental factors that contribute to this instability.  This can be achieved through genomic studies, patient stratification, systems biology and personalized medicine approaches. By understanding and addressing drug target instability, we can improve the efficacy of drugs across diverse patient populations and reduce health disparities.

At Intellomx we interrogate OMICS data sets from patient tissue to determine the molecular features that drive pathology at the systems level. Our proprietary AI and machine learning approaches allow us to assign a rank to gene products based on their degree of influence across a network compared to non-disease controls. This tool helps identify drug targets and biomarkers.

Using this approach we are also able to understand how stable a given gene product is across the population from which the OMICS data was derived and the impact that instability might have on a biological system. Using a gene product's ability to predict itself we can understand the gene product’s consistency of representation in the population. This allows us to prioritise gene products that are both influential AND stable. There’s little point in pursuing a target that’s highly influential in a small number of instances but will not manifest as stable across a typical or even selected patient population. Yet this happens time and time again in drug development as clinical trials are planned and executed blind to how stable a target protein is across the patient population to be tested.

Conclusion

Drug target instability across patient populations is a significant challenge in drug development. It can impact the efficacy of drugs in clinical trials and in patients, leading to variable patient responses, wasted R&D investment and potential health disparities. Addressing this issue using data available to us today, we can improve the success of therapeutics and ensure that they are effective for the right patient populations, significantly mitigating clinical development risks caused by a lack of appreciation of target stability.