Three Strategies To Minimize Clinical Development Costs
The clinical trials necessary to bring a new molecular entity (NME) to market are costly—around $48 million per drug, according to a 2020 study. However, by improving efficiency, drug developers can reduce costs and enhance the probability of success across their pipeline.
Current strategies that help biopharmaceutical companies reduce clinical trial costs fall into two categories:
- Technologies that capture greater amounts of higher-quality data
- Methods to ensure that model systems and patient cohorts used in the development of an NME accurately represent the target population
Developers running oncology clinical trials are especially focused on increasing efficiency since the trials they lead have grown longer and more complicated. At the same time, the COVID-19 pandemic inspired innovations to accelerate the discovery of new therapies. Used alone or in combination, each of the tactics described in this article help accelerate drug discovery and development, ultimately benefiting patients.
Use of AI to predict drug behavior early on
During preclinical research, artificial intelligence (AI), deep machine learning and physics-based methods can help identify drug candidates based on predicted molecular behavior before assessing NMEs in costly and time-consuming experiments. The process may include leveraging AI algorithms early in development to aid in molecule design and testing to select candidates that will undergo further testing in traditional wet lab experiments.
Additionally, AI and machine learning can model digitally simulated human organs. When informed by medical records and diagnostic and pathologic information, these digital organs can help scientists select the best treatment for a disease. Notably, this strategy recently enabled the rapid search for SARS-CoV-2 inhibitors.
Rely on high-quality materials
Excellent quality control is of the utmost concern throughout the drug development process: A sub-par manufacturing process can lead to safety concerns and costly setbacks. And difficulty gathering accurate data from patients can lead to unanswered questions. To avoid these expensive pitfalls, manufacturers should conduct testing to ensure NMEs are of the highest quality. Further, during a clinical trial, developers should consider using devices that simplify and improve data acquisition so that any drug product – and information about its effects – meets or exceeds all standards.
For example, when manufacturing CAR T-cell therapies, highly accurate and precise quality control methods ensure that each batch is safe and effective. Manufacturing CAR T-cell therapies involves extracting a patient's T cells and introducing the therapeutic chimeric antigen receptor (CAR) gene. DNA testing can then count CAR copy number to ensure the cells do not have too many CAR transgenes or too few, which would alter their potency.
While developers commonly use quantitative PCR (qPCR) for nucleic acid testing and quantification, this technique requires the preparation of a standard curve to interpret results, which introduces the potential for user bias and reduces sensitivity. For this reason, developers turn to Droplet Digital PCR (ddPCR) technology when evaluating the quality of each batch of CAR T cells. ddPCR technology directly counts DNA molecule by molecule and does so without the need for standard curves. Thus, the assay design renders ddPCR technology sensitive enough to detect as little as one copy of the CAR transgene. Furthermore, ddPCR assays can identify even trace levels of dangerous contaminants like bacteria or replication-competent viruses, ensuring the highest standard of safety.
Draw insight from patient DNA
Clinical trials become more costly as they expand to include more patients and run for longer time periods. Therefore, tactics to reduce the number of patients per trial and strategies to determine treatment efficacy sooner can save drug developers both time and money.
Since somatic mutations rather than anatomical location tends to be the major driving factor in cancer development, clinical trials generally proceed most efficiently and effectively when patients are placed according to their mutational profile. Large medical centers often use next generation sequencing (NGS) to conduct broad mutational screening on patients, which aids in diagnosis and informs treatment if druggable mutations are found. For treatment, an oncologist may prescribe on-market therapy or may enroll the patient in a clinical trial suited to the patient’s cancer type and disease stage.
This practice enables clinicians to screen hundreds to thousands of mutations in a single assay; however, labs should complement screens of this breadth with a highly sensitive reflex testing technology. This dual strategy allows labs to assess druggable edge cases, where NGS results cannot conclusively determine whether or not a mutation is present but a reflex technology such as ddPCR can provide confirmation. Not only can pairing NGS with a sensitive reflex technology like ddPCR ensure that more patients with druggable mutations receive the proper treatment, this system can also accelerate how quickly that treatment is administered. While it can take several days for an NGS experiment to return results, ddPCR can provide same-day results. All told, this optimized screening method is commonly used in large medical centers, but smaller community settings where most patients receive treatment are still in the process of adopting the practice. As labs that serve smaller communities adopt NGS and ddPCR technology platforms, they will be able to screen patients more extensively and enroll greater numbers of eligible patients in clinical trials. The influx of patients would help to shorten “open time” of trials and the overall timeline leading to therapy approval.
Additionally, developers could reduce clinical trial costs and increase their bandwidth by reducing how long their trials must run. Oncology trials, which tend to run 14–18 months longer than other trials, would benefit the most. The standard endpoint for these trials is survival, but some researchers are working to establish highly sensitive circulating tumor DNA (ctDNA) analysis as a more precise biomarker of clinical efficacy. The prediction: ctDNA analysis can more quickly and accurately indicate a tumor’s response to treatment.
As therapies become more advanced and complex, so must the trials assessing their efficacy. Drug developers can take advantage of new and emerging technologies to evaluate therapeutic candidates with greater rigor and efficiency while more quickly bringing beneficial treatments to those that need them most.
About the author:
Jeremiah McDole is Oncology Segment Manager at Bio-Rad Laboratories. He received his PhD 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.
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