Three Strategies to Minimize Clinical Development Costs

The clinical trials required to bring a new molecular entity (NME) to market are expensive — about $48 million per drug, according to 2020 survey. However, by improving efficiency, drug developers can reduce costs and increase the likelihood of success throughout their entire process.

Current strategies that help biopharmaceutical companies reduce clinical trial costs fall into two categories:

  • Technologies that capture larger amounts of higher quality data
  • Methods to ensure that model systems and patient cohorts used in NME development accurately represent the target population

Oncology clinical trial developers are particularly focused on increasing efficiency because the trials they lead have became longer and more complex. At the same time, the COVID-19 pandemic has inspired innovation to accelerate the discovery of new therapies. Used alone or in combination, each of the tactics described in this article helps accelerate drug discovery and development, ultimately benefiting patients.

Using AI to Early Predict Drug Behavior

During preclinical research, artificial intelligence (AI), deep machine learning, and physics-based methods can help identify drug candidates based on predicted molecular behavior before evaluating NMEs in expensive and time-consuming experiments. The process may involve leverage AI-algorithms early in development to aid in the design and testing of candidate molecules 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 pathology information, these digital organs can help scientists choose the best treatment for a disease. In particular, this strategy recently enabled a rapid search for SARS-CoV-2 inhibitors.

Trust in high-quality materials

Excellent quality control is of utmost concern during the drug development process: a below-par manufacturing process can lead to safety concerns and costly failures. And the difficulty of collecting accurate data from patients can lead to unanswered questions. To avoid these costly pitfalls, manufacturers must conduct testing to ensure that NMEs are of the highest quality. Additionally, during a clinical trial, developers should consider using devices that simplify and improve data collection so that each drug product—and information about its effects—meets or exceeds all standards.

For example, when CAR T-cell therapies are manufactured, highly accurate and precise quality control methods ensure that each batch is safe and effective. The production of CAR T-cell therapies involves extracting the patient’s T cells and introducing the therapeutic chimeric antigen receptor (CAR) gene. The DNA test can then count the number of CAR copies to ensure that the cells do not have too many or too few CAR transgenes. which would alter their efficacy.

While developers typically use quantitative PCR (qPCR) to test and quantify nucleic acids, 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 assessing 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 makes the ddPCR technology sensitive enough for detection only one copy of the CAR transgene in a sample. Additionally, ddPCR assays can identify even trace amounts of hazardous contaminants such as bacteria or replication-competent virusesensuring the highest standard of safety.

Get insight from the patient’s DNA

Clinical trials become more expensive as they expand to include more patients and run for longer periods of time. Therefore, tactics to reduce the number of trial patients and strategies for earlier determination of treatment efficacy can save drug developers time and money.

Because somatic mutations, rather than anatomic location, are usually the primary driver of cancer development, clinical trials typically proceed most efficiently and effectively when patients are assigned according to their mutational profile. Large medical centers often use next-generation sequencing (NGS) to perform extensive mutational screening of patients, which helps with diagnosis and informs treatment if mutations are found that can be used for drugs. For treatment, the oncologist may prescribe an over-the-counter therapy or may enroll the patient in a clinical trial appropriate for the patient’s type of cancer and stage of the disease.

This practice allows clinicians to screen hundreds to thousands of mutations in a single assay; however, laboratories must complement screens of such range with highly sensitive reflex testing technology. This dual strategy allows labs to evaluate druggable edge cases where NGS results cannot definitively determine whether a mutation is present or not, but a reflex technology such as ddPCR can provide confirmation. Not only does pairing NGS with a sensitive reflex technology like ddPCR ensure that more patients with druggable mutations receive appropriate treatment, this system can also speed up how quickly that treatment is administered. Although it can take several days for an NGS experiment to return results, ddPCR can provide same-day results. In general, this optimized screening method is commonly used in large medical centers, but smaller communities where most patients receive treatment are still in the process of adopting the practice. As laboratories serving smaller communities adopt NGS and ddPCR technology platforms, they will be able to screen patients more broadly and enroll a greater number of eligible patients in clinical trials. The influx of patients would help shorten the “open time” of trials and the overall timeline leading to a therapy’s approval.

In addition, developers could reduce the cost of clinical trials and increase their bandwidth by reducing the length of their trials. Oncology studies that tend to run 14–18 months more compared to other attempts, it will have the most benefit. The standard endpoint for these trials is survival, but some researchers are working to establish a high-sensitivity assay of circulating tumor DNA (ctDNA) as a more accurate biomarker for clinical efficacy. The prognosis: ctDNA analysis can more quickly and accurately show a tumor’s response to treatment.

Conclusion

As therapies become more advanced and sophisticated, so must the trials evaluating 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 who need them most.

About the author:

Jeremiah McDole is an oncology segment manager at Bio-Rad Laboratories. He in Neuroimmunology from the University of Cincinnati and spent his postdoctoral years in a number of successful research projects in the Department of Immunology at Washington University School of Medicine in St. Louis.

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