Why Biomarker Intelligence is Becoming Essential for Precision Drug Development

The drug development process is becoming more targeted.

Rather than wait to identify patients who will respond to a drug during clinical trial, pharmaceutical companies are now attempting to understand during clinical development which of the patients will be the most likely to benefit from a drug.

This change is one of the reasons why biomarker intelligence has grown in significance over the years.

There are many uses of biomarkers that can give useful information on the biology of the disease, reaction to treatment, patient characteristics, and safety. Incorporating biomarker data with clinical trial, drug pipeline and mechanism-of-action information can provide drug developers with a better understanding of the way a therapy is developing and where it might have the most potential.

Biomarker intelligence is more than a scientific research tool for pharmaceutical and biotech companies. It is increasingly emerging as integral component of clinical drug development and strategic decision making.

What is Biomarker Intelligence?

Biomarker is a measurable measurement that can give data about a biological process, disease or treatment. A biomarker can be used for the purposes of identifying risk of disease, predicting response to therapy, tracking disease or treatment response, and evaluation of safety depending on its use.

Biomarker intelligence goes further than this information.

It includes data collection, organization, analysis and linkage of biomarker information with other drug development data.

Examples of such questions could be:

• Biomarkers that are being used in clinical trials
• Drugs that have particular biomarkers
• The sponsors who are developing therapies based on biomarkers
• How are biomarkers being used for patient selection?
• Which mechanisms are associated with new biomarkers?
• At which drug development phases are particular biomarkers being used?

These relationships can enable a company to grasp the context of a clinical program, as well as the scientific approach it's taking.

The Importance of Biomarkers in the Drug Development Process

There is a tremendous amount of data produced in the clinical drug development process. Researchers need to know if a drug is being delivered to its target and if it is impacting the pathway under investigation and if it is having a meaningful effect in the right patient population.

Biomarkers can help provide that additional layer of evidence.

In early research, biomarkers can be used to gain insights into disease mechanisms and to assess possible targets. In the clinical development phase they may assist in patient selection, treatment monitoring, dose determination and biological response evaluation.

This has implications for several stages of drug discovery and development, making biomarkers relevant.

Early Development

Prior to a drug being taken into large clinical trials, researchers should have evidence of the candidate drug's impact on the intended biological pathway.

Biomarker data can be used to show biological activity and help to understand the interaction of a candidate with its target.

This information can prove useful when many possible drug candidates are being tested for the same disease.

Phase I and Phase II

Early clinical studies give information on safety, pharmacokinetics, pharmacodynamics and preliminary efficacy.

These clinical findings can be correlated with the biological changes occurring in patients with the use of biomarkers. This can be particularly valuable when companies are seeking to gain insight into the reasons for differential patient responses in Phase II.

A good biomarker plan can thus support sponsors to precision select patient populations and inform decisions on further development.

Phase III

Phase III trials are larger, and they aim to give more detailed evidence of effectiveness and safety.

In this stage, the information from the biomarkers can be used to help the sponsor decide if a specific biological characteristic is linked to treatment response or not, or to select or enrich patients for evaluation of treatment response.

FDA has identified several potential applications of biomarkers in drug development, such as patient selection, treatment allocation, dose selection, treatment monitoring and treatment response assessment.

Biomarker Intelligence and Precision Medicine

Difference between patients is key to precision medicine.

Two patients can have the same diagnosis, different molecular characteristics of the disease, or different drivers of disease, and different responses to treatment. Well-known therapies could be less effective in other sub-groups.

Biomarker intelligence can be incredibly useful here.

Companies can gain a deeper understanding of patient sub-groups and the biological aspects of treatment outcomes using biomarker data in conjunction with clinical and molecular data.

This can help answer one of the key questions drug developers have:

Is the drug not working for all of its target population, or is there a missing target population?

That distinction can have an impact on clinical development strategy.

Biomarker Databases, Now Playing an Increasing Role

Companies have to keep track of more information as the development of biomarkers grows.

A well-defined biomarker database can assist in the categorization of information by biomarker, disease, drug, sponsor, mechanism of action, clinical trial, and stage of development.

This enables research teams and strategy teams to go past basic searches.

A team might, for instance, explore in one project what biomarkers are being explored in other projects and how the strategies of the various sponsors compare.

This can uncover new research trends that wouldn't otherwise be apparent through a traditional pipeline analysis.

Biomarker Intelligence Can Complement Competitive Intelligence

Drugs are not developed by pharma companies in isolation. There are multiple sponsors pursuing the same target, indication or MOA.

Biomarker strategies can, therefore, be an added dimension of competitive intelligence monitoring.

A company can make a comparison:

Competition amongst the clinical programs was based on biomarkers.

• Patient selection strategies
• Mechanisms of action
• Development phases
• Trial endpoints
• Sponsor activity
• Biomarker-based clinical performance

This information could be used by teams to understand the manner in which the competition is setting up its programs.

It also can be used to identify new programs early on in the drug pipeline when there are still opportunities for partnerships, licensing or acquisition.

Markers, Licensing Decisions

The Biomarker data may also be helpful for business development teams.

Factors considered for an external asset are usually clinical stage, efficacy, safety, market potential, intellectual property, and competitive positioning.

Another important layer can be added with Biomarker strategy.

For instance, a differentiated biomarker approach could provide an asset with a very specific patient population or a more compelling rationale for mechanism of action.

When combined with clinical trial outcomes, pipeline activity, and intelligence about sponsors, the business development team can create a more comprehensive picture of an opportunity.

Regulatory Context Matters Too

Biomarker intelligence isn't only limited to scientific publications or clinical trials. Regulatory context is also important.

The FDA has established a Biomarker Qualification Program to help evaluate biomarkers for particular applications in drug development. The qualification is not a general approval of a biomarker for all purposes, but rather to a specific use.

This distinction is relevant when considering the maturity and potential relevance of a biomarker.

In the case of companies that have a business model of competitive or pipeline research, it is easier to have a full picture of the biomarker's position in drug development based on the global tracking of regulatory developments as well as clinical and scientific evidence.

How can companies improve their Biomarker Intelligence?

As precision medicine becomes more complicated, relying on clinical trial data is not always sufficient.

During the trial record, a development phase, sponsor, indication, intervention and recruitment status may appear. Biomarker intelligence can provide information on the biology of the program.

The combination of these datasets enables companies to find the correlation between:

Biomarkers → Mechanisms of Action → Drugs → Clinical Trials → Sponsors → Patient Populations

The integrated perspective can help with decision-making in clinical development, competitive intelligence, licensing, portfolio management and market research.

Clival Database Helps to Support Biomarker Intelligence

Finding more data is the easy part of it for pharma and biotech teams. It is linking the correct data together to assist in making a business decision.

Clival Database combines Clinical trial intelligence, Drug and Pipeline data, Sponsor intelligence, Therapeutic Area data and Biomarker/MOA data.

This wider perspective can assist teams in exploring trends in emerging biomarkers, comparing development programs, assessing pipeline opportunities, and exploring licensing and strategic opportunities.

In the field of precision medicine, companies that leverage bio-signals and clinical and competitive intelligence will have an advantage in making decisions about their development.

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