Five Ways to Minimize Drug Development Costs
Contributed Commentary by Jeremiah McDole, Oncology Segment Manager at Bio-Rad Laboratories
November 11, 2022 | The worldwide population is aging, and the prevalence of many diseases is on the rise, making rapid drug discovery and approval ever more critical. However, the cost of bringing a new drug to market is substantial. A 2020 study investigating the recent approval of 101 drugs found the total median cost of pivotal trials was an incredible $48 million per approval. Using newer technologies, drug companies may be able to reduce costs associated with development and ultimately enhance their odds of successful approvals by improving the efficiency of clinical trials. From bench to bedside, this article will discuss five ways drug developers can reduce their drug pipeline costs.
Predict Drug Behavior Using AI
To speed up the identification of drug candidates in the discovery phase, researchers can leverage artificial intelligence (AI). Molecule design and testing can be performed in-silico, reducing the need to run physical experiments only as a means of orthogonal validation.
Further, AI can be combined with digitally simulated human organs, informed by 3D image-based medical records, diagnostic details, and pathological information, to expedite drug candidate analysis. This method was successfully used in the search for SARs-CoV-2 inhibitors. Using these strategies in tandem increases the speed of discovery and reduces many associated costs, including the need to run expensive animal-based experiments in early-phase discovery.
Bring a New Level of Precision to Quality Control
Another effective way to speed up drug discovery involves implementing more precise quality control methods to limit contamination and reduce potency issues in the drug manufacturing process. As biologic drugs become increasingly common, developers need reliable ways to characterize biological composition. For instance, scientists have turned to tools such as Droplet Digital PCR (ddPCR) technology to aid in the development of CAR T cell therapies by detecting impurities and measuring potency.
This technology provides ultrasensitive and absolute nucleic acid quantification, producing results free of user bias without requiring standard curves. It is ideal for monitoring low-abundance targets, targets in complex backgrounds, and targets displaying subtle changes in expression or quantity that are undetectable by quantitative PCR (qPCR).
Enrich Patient Populations
The effort required to broadly screen potential patient populations for clinical trial enrollment can be dramatically reduced with next-generation sequencing (NGS). NGS is capable of detecting hundreds to thousands of germline or somatic mutations with a single assay. This approach can indicate an individual’s eligibility for a number of trials enrolling concurrently. This reduces the need to administer multiple single-gene tests and decreases the risk of missing an eligible individual that can benefit from trial enrollment. However, instances in which a mutational result falls below the limit of detection of NGS or targeted genetic analysis is needed within 24-48 hours require a reflex technology, such as ddPCR. The efficient workflow, rapid analysis, and ultra-sensitivity of ddPCR make it an ideal candidate for this use-case.
Use Remote Patient Monitoring
The use of wearable devices to monitor patients' vital signs cuts costs by reducing how often trial participants must travel and visit healthcare professionals. It can also improve patient care and safety, for example, by monitoring body temperature to identify cancer patients about to experience sepsis and triggering intervention before requiring a costly trip to the ICU.
With this approach, researchers can unobtrusively measure participants’ data while improving patient compliance. This strategy ultimately provides a clearer picture of treatment efficacy for investigators.
Establish Earlier Clinical Endpoints
Cancer therapeutics make up a large proportion of drugs in development, but these trials often last longer than other clinical trials, partly because patient survival is a standard endpoint. The longer a clinical trial runs, the more it costs. Therefore, researchers are working to establish the predictive value of circulating tumor DNA (ctDNA) analysis as an early clinical endpoint to evaluate therapeutic efficacy.
Groups like Friends of Cancer Research are conducting studies using ddPCR technology to establish when ctDNA status can signify remission status within a patient. Scientists predict that ddPCR is more rapid, cost-effective, and sensitive than NGS, and safer than repeated imaging, while enabling clinicians to accurately assess tumor response.
Through the systematic reduction of clinical costs paired with improved data acquisition and efficiency, future trials may produce higher-quality drugs at lower costs. By streamlining pipelines, drug developers can alleviate their own financial burden, creating an opportunity to lower costs for healthcare systems and patients. And with higher-quality drugs, clinicians may find themselves better equipped to treat complex diseases.
Jeremiah McDole received his Ph.D. in Neuroimmunology from the University of Cincinnati and spent his post-doctoral years on a number of successful research projects in the immunology depart at Washington University School of Medicine in St. Louis. He can be reached at Jeremiah_McDole@bio-rad.com.