Three strategies to minimize clinical development costs

The clinical trials required to bring a new molecular entity (NME) to market are costly — around $48 million per drug, according to a Study 2020. However, by improving efficiencies, drug developers can reduce costs and increase the likelihood of success across their pipeline.

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

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

Developers conducting clinical oncology studies have a particular focus on increasing efficiency, as the studies they have led have done so become longer and more complicated. At the same time, the COVID-19 pandemic inspired innovation to accelerate the discovery of new therapies. Each of the tactics outlined in this article can be used individually or in combination to accelerate drug discovery and development, ultimately benefiting patients.

Using AI to predict drug behavior early

In 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 costly and time-consuming experiments. The process may involve leverage AI Algorithms early in development to assist in molecule design and testing to select candidates to undergo further testing in traditional wet lab experiments.

In addition, AI and machine learning can model digitally simulated human organs. Based on medical records and diagnostic and pathological information, these digital organs can help scientists choose the best treatment for a disease. Especially this strategy recently enabled the rapid search for SARS-CoV-2 inhibitors.

Rely on high-quality materials

Excellent quality control is paramount throughout the drug development process: a subpar manufacturing process can lead to safety concerns and costly setbacks. And difficulties in collecting 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. Additionally, during a clinical trial, developers should consider using equipment that simplifies and improves data collection so that each drug product—and information about its effects—meets or exceeds all standards.

For example, in the manufacture of CAR T-cell therapies, highly accurate and precise quality control methods ensure that every batch is safe and effective. The manufacture of CAR T-cell therapies involves the extraction of a patient’s T-cells and the introduction of the therapeutic chimeric antigen receptor (CAR) gene. DNA testing can then count the CAR copy number to make sure the cells don’t have too many CAR transgenes or too few, which would alter their potency.

While developers often use quantitative PCR (qPCR) to test and quantify nucleic acids, this technique requires the creation of a standard curve to interpret the results, which introduces the possibility of 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 molecules without the need for standard curves. Thus, the assay design makes the ddPCR technology sensitive enough to be detected no more than a copy of the CAR transgene in a sample. In addition, ddPCR assays can detect even trace amounts of hazardous contaminants such as Bacteria or viruses capable of replicatingto ensure the highest security standard.

Gain insights from patient DNA

Clinical trials become more expensive as they extend to more patients and run over longer periods of time. Therefore, drug developers can save both time and money by reducing the number of patients per study and determining treatment efficacy sooner.

Since somatic mutations, rather than anatomical location, are the main driving factor in cancer development, clinical trials are generally most efficient and effective when patients are placed according to their mutational profile. Large medical centers often use next-generation sequencing (NGS) to perform broad mutation screening in patients, aiding diagnosis and informing treatment when drug-ready mutations are found. For treatment, an oncologist may prescribe a commercially available therapy or enroll the patient in a clinical trial that is appropriate for the patient’s cancer type and disease stage.

This practice allows clinicians to screen hundreds to thousands of mutations in a single assay; However, labs should supplement screens of this width with highly sensitive reflex test technology. This dual strategy allows laboratories to evaluate drug-worthy edge cases where NGS results cannot unequivocally determine whether a mutation is present or not, but a reflex technology such as ddPCR can provide confirmation. Combining NGS with a sensitive reflex technology such as ddPCR can not only ensure that more patients with drug-ready mutations receive the right treatment, this system can also accelerate the delivery of that treatment. While it can take several days for an NGS experiment to provide results, ddPCR can provide same-day results. All in all, this streamlined screening method is commonly used in large medical centers, but smaller community facilities, where most patients are treated, 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 comprehensively and enroll a larger number of eligible patients in clinical trials. The influx of patients would help reduce the “open time” of trials and the overall timeframe to drug approval.

In addition, developers could reduce the cost of clinical trials and increase their scope by shortening the duration of their trials. Oncology studies that are usually ongoing 14-18 months longer than other studies would benefit the most. The standard endpoint for these studies is survival, but some researchers are working to establish a highly sensitive analysis of circulating tumor DNA (ctDNA) as more accurate Biomarkers of clinical efficacy. The Prediction: ctDNA analysis can more quickly and accurately indicate a tumor’s response to treatment.

Conclusion

As therapies become more advanced and complex, the studies to evaluate their effectiveness must also be conducted. Drug developers can leverage new and emerging technologies to evaluate therapeutic candidates with greater rigor and efficiency, while bringing beneficial treatments more quickly to those who need them most.

About the author:

Jeremiah McDole is an oncology segment manager at Bio-Rad Laboratories. He received his PhD in neuroimmunology from the University of Cincinnati and spent his postdoctoral years on a series of successful research projects in the field of immunology at Washington University School of Medicine in St. Louis.

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