Advancing Cardiovascular Drug Discovery: Translational Framework Leverages Genetic and Proteomic Insights

By Dr. Chinta SidharthanReviewed by Lily Ramsey, LLMAug 19 2024

The advances in omics technology have resulted in the generation of genome and proteome data at the population scale. The development of numerous advanced genomic and proteomic analytical tools has also provided a unique opportunity to identify protein targets for therapeutical use in cardiovascular disease.

In a recent study published in Npj Cardiovascular Health, researchers proposed a validated framework to translate these recent advances in genoproteomics into clinical applications in cardiovascular drug development.

Study: A translational framework of genoproteomic studies for cardiovascular drug discovery. Image Credit: Andrii Zastrozhnov/Shutterstock.com

Background

One of the leading causes of mortality continues to be cardiovascular disease, which is responsible for close to a third of the global mortality rates, with stroke and ischemic heart disease being the two most prevalent cardiovascular events.

Lifestyle modifications and pharmaceutical interventions, such as drugs targeting low-density lipoprotein (LDL) levels, have been successful in lowering the incidence of premature cardiovascular events by almost 80%.

However, safety concerns and decreased clinical efficacy have led to the gradual stagnation of cardiovascular disease-associated drug development. Close to ten years of extensive research in drug development for cardiovascular disease has only resulted in a success rate of less than 10%.

The cause for this low success rate is believed to be unintended effects of the candidate drugs and the targeting of non-causal biomarkers. These trends indicate the need for more innovative approaches to cardiovascular drug development.

About the Study

The present study addressed the shortage of validated methods to translate the genoproteomic data and advanced genomics and proteomics analytical tools into clinical applications to develop more effective drugs for cardiovascular diseases.

Numerous large-scale databases, including biobanks from various countries such as the United Kingdom, Japan, Finland, China, and the Netherlands and the All of Us Research Program from the United States, have made a wide range of genoproteomic data available for research.

The use of these databases in conjunction with advanced analytical methods such as genome-wide association studies and Mendelian randomization presents an opportunity to address the concerns of adverse reactions and side effects, as well as the identification of causal protein targets.

However, translating the findings from genome-wide association studies into clinical applications for drug development continues to present challenges as a large portion of the hits from these studies are in the non-coding regions and influence the progression and susceptibility to the disease. This leaves a significant portion of the clinical and functional variations and their effects on the proteins targeted by the drugs undiscovered.

The researchers presented a translational analytical framework that integrated the genomic, proteomic, and phenomic contexts of drug discovery by using Mendelian randomization, and a range of other observational and genetic analysis tools to explore the interactions between gene variants, protein expression, and disease outcomes.

Outcomes

The framework presented in the study consisted of four stages through which genomic and proteomic information on disease-related variations were translated to clinical applications for drug discovery.

The first step of the framework was to screen for causal biomarkers, which consisted of a broad-scale proteomic screening and proteome-wide Mendelian randomization analysis to identify potential biomarkers that could play a causal role in the disease.

In the second stage, genetic analyses are conducted to understand the specific biological pathways and mechanisms these causal biomarkers contribute to disease risk. This also helps identify the proteins that can potentially be targeted to modulate these biological pathways. The researchers also discussed various genetic analyses used in this stage.

The third stage employs phenome-wide Mendelian randomization to explore the possible side effects of targeting the proteins identified in stage two. Here, a wide range of possible health outcomes that could occur by modifying the target protein are examined to determine potential unintended adverse effects.

The final stage involves a hypothetical randomized control trial, such as a pragmatic trial or a trial emulation, to evaluate the impact of the therapeutic intervention and determine potential risks and expected benefits from targeting the protein identified through the first three stages.

The study also validated the framework by applying it to replicate the effect of the current drugs that aim to lower lipid levels.

The protein targets for lowering lipid levels were apolipoprotein B to lower the LDL and triglyceride levels, lipoprotein-a, and low-density lipoprotein receptor to lower LDL-C levels.

Conclusions

The study showed that the framework could effectively translate large-scale multi-omics data to tangible clinical applications in drug discovery.

The researchers validated the framework using the example of cardiovascular drug discovery. They demonstrated the advantages of the framework in helping overcome the challenges of unintended adverse effects and non-causal target proteins that are currently hindering cardiovascular drug discovery.