Leen Kawas Details Key Strategies for Increasing Clinical Trials’ Success Rates * California Recorder

In the multifaceted drug development arena, a successful clinical trial paves the way for potential FDA drug candidate approval. For perspective, a successful drug development cycle typically extends for 10 to 15 years. This complex process costs approximately $1 billion per approved drug candidate.

However, most drug candidates never make it through the clinical trial phase. In fact, 90 percent of in-development drugs fail to successfully navigate this multi-year process.

This less-than-optimal outcome may result from one or more contributing factors. Leen Kawas, Ph. D. discussed the reasons for clinical trial failures and offered recommendations to help improve trial outcomes.

Leen Kawas is Propel Bio Partners’ Managing General Partner. This biotech-focused venture capital firm partners with start-up and early-stage businesses requiring financial, operational, and technological support. An accomplished biotechnologist, Kawas previously served as Athira’s Chief Executive Officer (or CEO). While there, she successfully managed multiple drug development cycles.

Today, Leen Kawas is well-informed about Propel Bio Partners’ clients’ drug development programs. Her diversified biotech experiences enable her to offer valuable insights that help position each firm for positive outcomes.

During the past several decades, researchers have implemented strategies to improve each drug development phase. To illustrate, technologists have streamlined the drug development candidate identification process. In addition, artificial intelligence (or AI) tools are integrated into drug design.

Next, robot-enhanced high-throughput screening automates millions of laboratory tests. Researchers also introduced new methods of drug toxicity prediction and testing.

Finally, the clinical trial patient selection sequence has been refined. Even with these advancements, however, the drug development success rate has seen minimal change.

Clinical research failures typically result from negative outcomes in one of two areas. To illustrate, a project-related failure means the management team hasn’t achieved its goals and/or met its deadlines. Budget overruns can derail an otherwise-viable research project.

A research failure means the team has been unable to statistically realize a specific goal. In a clinical trial, this means the team has failed to prove a drug’s efficacy or obtained otherwise-questionable outcomes. An unexpectedly toxic drug candidate, or one with untenable side effects, also contributes to research failures.

5 Factors that can Derail Clinical Trials

Each multi-phase clinical trial integrates multiple stakeholders and components. discussed five factors that can contribute to a negative outcome.

Inexperienced Project Manager

A clinical trial manager who lacks risk management prediction experience, and who cannot motivate their team, does not set the stage for a positive outcome. This suboptimal performance can result from a lower-ranked researcher who assumes a higher-level role without expertise in clinical trial or larger project management.

This new project manager may fail to include key decision makers in the process and/or implement unrealistic completion deadlines. These factors can together lead team members toward disengagement and lackluster trial performance.

Overly Complex Clinical Trial Protocols

Some clinical trials attempt to address an excessive number of questions. This leads to an unnecessarily complex trial protocol structure. Subsequent protocol amendments may seek to delete approximately half of the trial’s beginning endpoints. Despite this effort to simplify the trial, this initial misstep could set the stage for trial failure.

Ethics-Based Concerns

Ideally, clinical trial researchers and team members will consistently act in accordance with ethical principles and standards. However, ethics breaches can take place in any trial phase. A conscious breach can be termed as “fraud” while an unconscious breach may be regarded as “sloppy” research.

In a worst-case scenario, these ethical lapses can bring high trial failure risk into the picture. This can seriously damage all stakeholders’ professional reputations.

Substandard Data Quality

Every successful clinical trial integrates reliable clinical data that informs

evidence-based results. This impactful data application enables study completion. A study’s lack of data quality surveillance leads to undesirable outcomes.

To illustrate, a team member’s inability to correctly understand measurement methods can impact measurements’ confidence levels. The measurement devices’ miscalibration can have the same negative impact. Both errors can affect clinical trial outcomes’ validity.

Patient Recruitment Challenges

Clinical trial managers frequently have difficulty recruiting a sufficient number of participants who meet predefined criteria. Patients’ lack of proximity to trial sites, and the individuals’ inability to commit to an extended trial regimen, are major factors.

Equally importantly, clinical trial managers often fail to recruit a highly diversified participant base. Individuals from varied ethnic and income groups are often excluded from consideration. Taking a bigger-picture view, this leads to trial results that are not reflective of a cohort’s actual population.

To ensure a clinical trial’s accuracy and validity, the trial manager must ensure that the trial outcomes are relevant to real-world patients in typical clinical settings. In addition, the trial results should remain valid under the Food and Drug Administration’s (or FDA’s) increasingly stringent approval criteria. Leen Kawas detailed three strategies to boost overall clinical trial success rates.

Optimal Study Design Selection

When a clinical trial integrates the best-possible study design, the stage is set for a successful outcome. Conversely, an inferior study design may affect the clinical trial’s ability to display a drug candidate’s efficacy.

A successful clinical trial should integrate site selection along with inclusion and exclusion parameters. A trial manager should also utilize artificial intelligence (or AI) tools to complete literature reviews, gauge eligibility standards viability, and estimate patient dropout levels.

Finally, the AI can evaluate multiple statistical methods associated with successful outcomes. Taken together, these factors can help optimize a clinical trial’s study design.

Successful Patient Recruitment

Across the United States, one-third of National Institutes of Health-associated clinical trials are unable to reach patient recruitment objectives. This has long been a major factor behind clinical trials’ failures. Influencing factors include lack of patient trial design participation, insufficient support, and unrealistic recruitment estimates.

Leen Kawas has long advocated for more patient recruitment diversity. Use of AI tools to analyze patients’ medical records and social media use can better identify viable participants. In turn, the AI tools’ use decreases the time and expense to achieve study recruitment goals.

High-quality Data Acquisition

Ensuring data integrity contributes to research results’ generalization potential. Informing involved team members about standardized protocols, creating a data management template, and overseeing data quality are key. Real-time data acquisition further optimizes data quantity and quality.

Leen Kawas’ clinical trials expertise expands her broad knowledge of the biotechnology industry. She emphasized that adherence to successful trials’ parameters will greatly increase the chances of productive clinical trial outcomes.

Originally published at https://californiarecorder.com on October 19, 2024.