Changing the landscape of R&D to build clinical success from the ground up

insights from industryAmanda JonesSenior Strategy Leader, Life Science ResearchRevvity

In this interview, NewsMedical talks to Amanda Jones at Revvity about the strategies and solutions available to revolutionize the landscape of R&D in clinical research.

Can you describe some of the underlying causes driving high attrition rates observed in the early phases of clinical trials?

It is well-documented that over 90 % of drugs that enter the clinical pipeline are destined for failure, with oncology having the lowest likelihood of success. Most of these failures occur in earlier trial phases where drug candidates are shown to lack clinical efficacy or have unmanageable toxicity. The root causes often are preclinical models that fail to accurately predict efficacy and toxicity and do not deal with the heterogeneity of the real-world patient population.

How are pharmaceutical companies trying to narrow the translational gap between basic research and clinical application?

Recent decades have seen pharmaceutical companies increasingly focus on addressing the rift between basic science and clinical practice. By fostering collaborations across academia and industry, the knowledge embedded in basic research is strengthening the transition to clinical development by providing methods to target disease and identify potential risks early. Equally important to translational success is identifying patient subgroups that are most likely to respond to treatment.

This results in the revision of the linear approaches to discovery to patient-centered research, which can provide novel diagnostic and therapeutic tools essential for translating preclinical science into humans.

clinical trialsImage Credit: Stock-Asso/Shutterstock.com

Can you discuss the impact of improved preclinical models on the probability of success of clinical trials?

Advancements in genetic and tissue engineering are enabling researchers to construct preclinical models that more faithfully recapitulate human disease. Micro-physiological systems such as organoids, organ-on-chip systems, or even humanized animal models can better predict clinical outcomes, meaning that go or no-go decisions can be made earlier in discovery, which increases the odds of drug candidates being successful in clinical trials. 

Despite the higher cost of running these more complex model systems, the potential savings to R&D costs are significant -- for example, organ-on-chip systems could deliver 10-26 % cost savings over a five-year period.

In addition, the predictive power of these models can aid in the design of clinical trials, including the selection of endpoints and patient stratification, which can lead to more targeted and efficient trials.

How is the use of artificial intelligence and machine learning technologies improving the efficiency of therapeutic selection?

By rapidly screening a vast set of data, artificial intelligence (AI) and machine learning (ML) technologies can connect, interpret, and decide, revolutionizing drug discovery. From designing drug candidates to evaluating and managing toxicity, AI is proving valuable across all stages of drug discovery.

However, AI / ML is not just enabling better drugs to be brought to market faster, functional precision medicine is an emerging field where patient derived cell models are tested against drug panels enabling predictions to be made on the best patient outcomes. A great example of an ongoing trial in this area includes the EXCYTE-2 study initiated by Exscientia, which looks to evaluate precision medicine platforms in patients with acute myeloid leukemia.

How are advancements in genomics influencing drug candidate selection in pharmaceutical companies?

Drug candidate selection has been transformed by the advancements in genomics over the last decade. Genome-wide CRISPR editing has emerged as a powerful tool for validating and prioritizing drug targets by allowing researchers to observe the effects of gene activation or suppression on cellular functions. This aids in the discovery of potential drug targets and the understanding of their mechanisms of action.

In addition, next-generation sequencing provides comprehensive insights into the genetic makeup of diseases and the selection of biomarkers, ensuring that selected candidate drugs are likely to be effective in the intended patient population, which is crucial for the development of personalized medicine approaches.

What is the role of genetic evidence for target validation today, and how has this changed the drug development process?

The integration of genetic insights into the early stages of drug discovery provides a reliable method for identifying and validating potential therapeutic targets. By leveraging data from genome-wide association studies and other genetic research, scientists can pinpoint genes that are directly involved in disease processes. This information can then be used to assess the relevance and druggability of targets, increasing the likelihood of clinical success.

In fact, studies have shown that drug targets with genetic support are twice as likely to lead to approved drugs, highlighting the value of genetic evidence in reducing the high attrition rates observed in drug development.

Additionally, the use of genetic data, such as directed knockdowns, in target validation can help predict potential adverse effects and identify suitable indications for treatment, making the drug development process not only more efficient but also more patient-centric.

clinical researchImage Credit: Gorodenkoff/Shutterstock.com

How has the use of biomarkers in clinical trials changed in recent years, and how does that impact the outcomes of clinical trials?

The use of biomarkers in clinical trials has undergone significant evolution in recent years, particularly in the field of oncology. Historically underutilized, biomarkers are now pivotal in the design and execution of clinical trials, leading to more targeted and efficient studies. For example, the use of biomarker-matched therapies in trials has improved outcomes in patients with specific cancer types through higher overall response rates and longer progression-free survival.

The impact of this change is profound, not only in terms of the potential for more effective treatments but also in terms of the possibility of reducing the overall cost of healthcare by identifying responders and non-responders early in the treatment process. As the use of biomarkers continues to grow, it is expected that clinical trials will become more streamlined, outcomes will improve, and personalized medicine will become the standard approach in treatment protocols.

What role do novel therapeutic modalities such as cell and gene therapies play in the current rise of clinical development productivity?

Cell and gene therapies represent a significant advancement in the treatment of various diseases, particularly rare diseases and hematological cancers. Their targeted nature helps to deliver greater clinical success, with treatments showing to be two to three times more likely to be successful than other types of treatments for similar conditions.

For example, therapies such as CAR-T/TCR for blood cancers have shown a three times higher likelihood of approval when entering phase 1 compared to the average oncology drug. Moreover, orphan gene therapies have outperformed the average drug at all stages of the clinical development process, demonstrating a remarkable potential to advance through the phases of clinical trials successfully. The success of these therapies in clinical trials is a testament to the precision and potential of genetic medicines to provide enduring treatments for complex diseases.

However, it is important to note that the field of gene therapy is still evolving, with ongoing challenges such as the high one-time costs and the complexity of manufacturing therapeutic agents at pharmacologic standards of quality and consistency. Despite these challenges, the clinical success of cell and gene therapies continues to grow, offering hope for patients and a promising direction for future medical research and treatment strategies.

References

  1. https://www.sciencedirect.com/science/article/pii/S135964461930042X#sec0015
  2. https://www.nature.com/articles/ng.3314
  3. ClinicalDevelopmentSuccessRates2011_2020.pdf (bio.org)

About Revvity

Revvity provides health science solutions, technologies, expertise, and services that deliver complete workflows from discovery to development, and diagnosis to cure.​

Revvity is pushing the limits of what’s possible in healthcare, with specialized focus areas in translational multi-omics technologies, biomarker identification, imaging, prediction, screening, detection and diagnosis, informatics and more.​

With a robust global network and localized agility, we serve a diverse range of organizations from pharmaceutical and biotech, to clinical labs, academia, and governments.​

Together with our customers and partners, we are united in impact, embracing the impossible to improve lives everywhere.


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