Clinical Trial Strategist Dr. Patil Demystifies the Truth of Clinical Trial Costs

Clinical Trial Strategist Dr. Patil Demystifies the Truth of Clinical Trial Costs

BIRMINGHAM, AL / ACCESSWIRE / August 26, 2022 / Global spending on clinical trials is expected to increase to $68.9 billion per year by 2025. The median cost of a trial is estimated to be $48 million and the number of patients is the single largest driver of the total cost of clinical trials. The average cost to bring a drug to market is $1.3B. This all in turn translates into growing costs of treatment.

Dr. Deepak Patil, Friday, August 26, 2022, Press release picture

Why are trials so expensive:

A study published by PhRMA found that the average cost for one patient in a US-based clinical trial was $36,500. This cost varies by therapy area – more expensive for rare and difficult to recruit patients (e.g. $59,500 per patient for cancer drugs) vs. easier to recruit patients ($16,500 per patient). Efficient patient identification, recruitment, and retention are extremely challenging. MESM estimated that ~85% of clinical trials are delayed, with 94% experiencing more than a month-long delay. The financial impact can be massive, ranging from $600k – $8 million for each day of delay.

Despite robust investment in data-driven patient identification, there is tremendous competition from major pharmaceutical companies for recruiting patients at renowned academic medical centers. There is a need to increase awareness of trials among the patient population, and enhance the ease of access and enrollment into clinical trials. Companies like SubjectWell, Trial Match, etc bridge the knowledge gap, but much of the opportunity remains unexplored.

We spoke with Dr. Deepak Patil, a real-world data expert, and life-sciences strategy consultant to understand levers for improving the design and execution of clinical trials. Below are some highlights of the discussion.

Breaking real-world data (RWD) silos

The industry’s capability to drive data-driven site and investigator identification needs to be supported by breaking silos of real-world data, encouraging anonymized insights sharing across stakeholders. Real-world data (e.g. clinical trial data, electronic health records, claims data, etc) is extremely siloed. Pieces of the data are owned by several entities (e.g. an EHR vendor could own clinical data for 10s of millions of patients, specialized labs own biomarker and genomics data, patient registries own data pertaining to patients with particular diseases, etc.). Dr. Patil,”..there is an opportunity for larger health technology firms to aggregate data more efficiently, design ecosystem partnerships benefiting all stakeholders. Breaking silos can result in better investigator and patient identification, and potential to deliver data-driven clinical trials.” Oracle’s acquisition of Cerner is a promising next step in aggregating healthcare data to drive value.

Designing with the patient at the center

Trial sponsors should consult patient advocates early in the process to incorporate meaningful endpoints addressing patients’ core needs, and ensure the trial design fits well within a patient’s journey, Dr. Pati said. “A glaucoma clinical trial that requires dilation of pupils before administration of the trial drug could require a patient to take an entire day off from work every time the patient visits the trial site. Moreover, patients might not be comfortable driving themselves to or back from the trial site. This has a significant financial and logistical burden and leads to trial dropouts. A trial sponsor could mitigate against the risk of trial dropouts by offering trial concierge systems, employing decentralized clinical trial modalities where possible, and offering a reasonable stipend acknowledging a day of lost work.”

Thinking outside the box

Dr. Patil also discussed a creative way to go beyond easily available data to create a winning clinical trial recruitment strategy. Dr. Patil,”(sic). It is also necessary to acknowledge macro and micro economic factors driving trial participation in assessing the potential to include ex-US sites in clinical trials. We have noticed that patients in few ex-US markets with significantly higher out-of-pocket costs, and fewer launched therapies for their condition are more interested in learning about and potentially participating in clinical trials. Additionally, site-level dynamics like the ease of site setup, historic assessments of the quality of data received from a site, and partnerships with specialty labs can be important factors in site selection. Eventually, a mix of US-based and ex-US sites meeting the criteria for a particular study can be engaged to limit the risk of poor enrollment and delays.”

Driving efficiency across the process

The emergence of promising Artificial Intelligence and Machine Learning startups like Owkin, Unlearn, AI, Deepen Lens, and AiCure will lead to efficient clinical trial design and deployment in the future.

Breaking down clinical trial costs

Creating and selling a new medicine is a complicated and long process, with the possibility of a roadblock at every step. The R&D process goes from basic science to preclinical and clinical research, to approval of novel treatments for patients given by bodies like healthcare providers and others. By finding these alternatives to cut trial costs, budgets will be able to find a new focus to help further streamline the process that results in these drugs playing a meaningful role in treatment.

Clinical trial costs are a complex expense to factor within the life sciences industry as reliable subjects, whether in relation to the pandemic or otherwise, become a precious resource. It is finding new ways to obtain this resource that could help companies become more efficient.

To contact, visit Dr. Deepak Patil’s Linkedin profile at linkedin.com/in/deepakdpatil or email at [email protected]

SOURCE: Dr. Deepak Patil

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https://www.accesswire.com/713639/Clinical-Trial-Strategist-Dr-Patil-Demystifies-the-Truth-of-Clinical-Trial-Costs



Published Fri, 26 Aug 2022 09:51:03 -0500

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