The clinical trials needed to bring a new molecular entity (NME) to market are expensive, about $48 million per drug, according to a study 2020. However, by improving efficiency, drug developers can reduce costs and improve the likelihood of success in their pipeline.
Current strategies that help biopharmaceutical companies reduce clinical trial costs fall into two categories:
- Technologies that capture larger amounts of better quality data
- Methods to ensure that model systems and patient cohorts used in the development of an NME accurately represent the target population
Developers who conduct oncology clinical trials are particularly focused on increasing efficiency since the trials they conduct have become longer and more complicated. At the same time, the COVID-19 pandemic has inspired innovations 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 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 evaluating NMEs in experiments expensive and time-consuming. The process may include leveraging AI algorithms early in development to help design and test molecules to select candidates for 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 pathological information, these digital organs can help scientists select the best treatment for a disease. In particular, this strategy recently enabled the rapid search for SARS-CoV-2 inhibitors.
Rely on high-quality materials
Excellent quality control is of the utmost importance throughout the drug development process: a substandard manufacturing process can lead to safety issues and costly setbacks. And the difficulty in collecting accurate data from patients can lead to unanswered questions. To avoid these costly pitfalls, manufacturers must perform tests to ensure NMEs are of the highest quality. Additionally, 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, very precise and precise quality control methods ensure that each batch is safe and effective. Manufacturing CAR T-cell therapies involves extracting T cells from a patient and introducing the chimeric antigen receptor (CAR) therapeutic gene. DNA testing can then count the number of CAR copies to ensure cells do not have too many or too few CAR transgenes, which would alter their potency.
While developers commonly use quantitative PCR (qPCR) for nucleic acid testing and quantification, this technique requires preparation of a standard curve to interpret results, which introduces potential for user bias and reduces the sensibility. 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 standard curves. Thus, the assay design makes ddPCR technology sensitive enough to detect as few as one copy of the CAR transgene. Additionally, ddPCR tests can identify even traces of dangerous contaminants like bacteria or viruses capable of replicatingensuring the highest level of security.
Take a glimpse of the patient’s DNA
Clinical trials become more expensive as they expand to include more patients and take place over longer periods of time. Therefore, tactics to reduce the number of patients per trial and strategies to determine treatment efficacy earlier can save drug developers time and money.
Since somatic mutations rather than anatomical location tend to be the primary determinant of cancer development, clinical trials generally run more effectively and efficiently when patients are placed based on their mutational profile. Large medical centers often use next-generation sequencing (NGS) to perform broad mutation screening on patients, which aids in diagnosis and informs treatment if drug mutations are found. For treatment, an oncologist may prescribe a treatment on the market or may enroll the patient in a clinical trial tailored to the type of cancer and stage of the patient’s disease.
This practice allows clinicians to screen for hundreds or even thousands of mutations in a single test; however, labs should supplement displays of this magnitude with highly sensitive reflex testing technology. This dual strategy allows laboratories to evaluate edge drug cases, where NGS results cannot conclusively determine whether or not a mutation is present, but reflex technology such as ddPCR can provide confirmation. Not only does the combination of NGS with sensitive reflex technology such as ddPCR ensure that more patients with drug mutations receive the correct treatment, but this system can also speed up the speed of delivery of that treatment. Although it can take several days for an NGS experiment to return results, ddPCR can provide same-day results. Altogether, this optimized screening method is commonly used in large medical centers, but smaller community facilities where most patients receive treatment are still adopting the practice. As labs serving smaller communities adopt NGS and ddPCR technology platforms, they will be able to screen patients more thoroughly and enroll more eligible patients in clinical trials. . The influx of patients would help shorten the “open time” of trials and the overall time to treatment approval.
Additionally, developers could reduce clinical trial costs and increase their bandwidth by reducing the length of their trials. Oncology trials, which tend to take place 14 to 18 months older than other trials, would benefit the most. The standard endpoint of these trials is survival, but some researchers are working to establish highly sensitive analysis of circulating tumor DNA (ctDNA) as a more precise endpoint. clinical efficacy biomarker. Prediction: cDNA analysis can more quickly and accurately indicate a tumor’s response to treatment.
As therapies become more advanced and complex, so must trials evaluating their effectiveness. Drug developers can take advantage of new and emerging technologies to evaluate therapeutic candidates with greater rigor and efficiency while delivering beneficial treatments faster to those who need them most.
About the Author:
Jeremiah McDole is Head of Oncology at Bio-Rad Laboratories. He earned his PhD in Neuroimmunology from the University of Cincinnati and spent his post-doctoral years on a number of successful research projects in the Department of Immunology at Washington University School of Medicine in St. Louis.